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DarkBench: Benchmarking Dark Patterns in Large Language Models | https://openreview.net/forum?id=odjMSBSWRt | [
"Esben Kran",
"Hieu Minh Nguyen",
"Akash Kundu",
"Sami Jawhar",
"Jinsuk Park",
"Mateusz Maria Jurewicz"
] | Oral | We introduce DarkBench, a comprehensive benchmark for detecting dark design patterns—manipulative techniques that influence user behavior—in interactions with large language models (LLMs). Our benchmark comprises 660 prompts across six categories: brand bias, user retention, sycophancy, anthropomorphism, harmful generation, and sneaking. We evaluate models from five leading companies (OpenAI, Anthropic, Meta, Mistral, Google) and find that some LLMs are explicitly designed to favor their developers' products and exhibit untruthful communication, among other manipulative behaviors. Companies developing LLMs should recognize and mitigate the impact of dark design patterns to promote more ethical Al. | Dark Patterns, AI Deception, Large Language Models | We introduce DarkBench, a benchmark revealing that many large language models employ manipulative dark design patterns. Organizations developing LLMs should actively recognize and mitigate the impact of dark design patterns to promote ethical Al. | 14257 | 2503.10728 |
RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style | https://openreview.net/forum?id=QEHrmQPBdd | [
"Yantao Liu",
"Zijun Yao",
"Rui Min",
"Yixin Cao",
"Lei Hou",
"Juanzi Li"
] | Oral | Reward models are critical in techniques like Reinforcement Learning from Human Feedback (RLHF) and Inference Scaling Laws, where they guide language model alignment and select optimal responses.
Despite their importance, existing reward model benchmarks often evaluate models by asking them to distinguish between responses generated by models of varying power.
However, this approach fails to assess reward models on subtle but critical content changes and variations in style, resulting in a low correlation with policy model performance.
To this end, we introduce RM-Bench, a novel benchmark designed to evaluate reward models based on their sensitivity to subtle content differences and resistance to style biases.
Extensive experiments demonstrate that RM-Bench strongly correlates with policy model performance, making it a reliable reference for selecting reward models to align language models effectively.
We evaluate nearly 40 reward models on RM-Bench.
Our results reveal that even state-of-the-art models achieve an average performance of only 46.6%, which falls short of random-level accuracy (50%) when faced with style bias interference.
These findings highlight the significant room for improvement in current reward models. | Reward Models, Language Models, Evaluation, Alignment | null | 13985 | null |
TopoLM: brain-like spatio-functional organization in a topographic language model | https://openreview.net/forum?id=aWXnKanInf | [
"Neil Rathi",
"Johannes Mehrer",
"Badr AlKhamissi",
"Taha Osama A Binhuraib",
"Nicholas Blauch",
"Martin Schrimpf"
] | Oral | Neurons in the brain are spatially organized such that neighbors on tissue often exhibit similar response profiles. In the human language system, experimental studies have observed clusters for syntactic and semantic categories, but the mechanisms underlying this functional organization remain unclear. Here, building on work from the vision literature, we develop TopoLM, a transformer language model with an explicit two-dimensional spatial representation of model units. By combining a next-token prediction objective with a spatial smoothness loss, representations in this model assemble into clusters that correspond to semantically interpretable groupings of text and closely match the functional organization in the brain's language system. TopoLM successfully predicts the emergence of a spatially organized cortical language system as well as the organization of functional clusters selective for fine-grained linguistic features empirically observed in human cortex. Our results suggest that the functional organization of the human language system is driven by a unified spatial objective, and provide a functionally and spatially aligned model of language processing in the brain.Neurons in the brain are spatially organized such that neighbors on tissue often exhibit similar response profiles. In the human language system, experimental studies have observed clusters for syntactic and semantic categories, but the mechanisms underlying this functional organization remain unclear. Here, building on work from the vision literature, we develop TopoLM, a transformer language model with an explicit two-dimensional spatial representation of model units. By combining a next-token prediction objective with a spatial smoothness loss, representations in this model assemble into clusters that correspond to semantically interpretable groupings of text and closely match the functional organization in the brain's language system. TopoLM successfully predicts the emergence of a spatially organized cortical language system as well as the organization of functional clusters selective for fine-grained linguistic features empirically observed in human cortex. Our results suggest that the functional organization of the human language system is driven by a unified spatial objective, and provide a functionally and spatially aligned model of language processing in the brain. | language modeling, topography, fMRI, neuroscience | We develop a transformer language model with topographically organized units predicting brain-like spatio-functional organization. | 13712 | 2410.11516 |
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows | https://openreview.net/forum?id=XmProj9cPs | [
"Fangyu Lei",
"Jixuan Chen",
"Yuxiao Ye",
"Ruisheng Cao",
"Dongchan Shin",
"Hongjin SU",
"ZHAOQING SUO",
"Hongcheng Gao",
"Wenjing Hu",
"Pengcheng Yin",
"Victor Zhong",
"Caiming Xiong",
"Ruoxi Sun",
"Qian Liu",
"Sida Wang",
"Tao Yu"
] | Oral | Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics.
We introduce Spider 2.0, an evaluation framework comprising $632$ real-world text-to-SQL workflow problems derived from enterprise-level database use cases.
The databases in Spider 2.0 are sourced from real data applications, often containing over 1,000 columns and stored in local or cloud database systems such as BigQuery and Snowflake.
We show that solving problems in Spider 2.0 frequently requires understanding and searching through database metadata, dialect documentation, and even project-level codebases.
This challenge calls for models to interact with complex SQL workflow environments, process extremely long contexts, perform intricate reasoning, and generate multiple SQL queries with diverse operations, often exceeding $100$ lines, which goes far beyond traditional text-to-SQL challenges.
Our evaluations indicate that based on o1-preview, our code agent framework successfully solves only 21.3\% of the tasks, compared with 91.2\% on Spider 1.0 and 73.0\% on BIRD.
Our results on Spider 2.0 show that while language models have demonstrated remarkable performance in code generation --- especially in prior text-to-SQL benchmarks --- they require significant improvement in order to achieve adequate performance for real-world enterprise usage.
Progress on Spider 2.0 represents crucial steps towards developing intelligent, autonomous, code agents for real-world enterprise settings.
Our code, baseline models, and data are available at [spider2-sql.github.io](spider2-sql.github.io) . | LLM Benchmark, Data Science and Engineering, Code Generation, Text-to-SQL, LLM Agent | A benchmark for enterprise-level Text-to-SQL involving complex databases, challenging tasks, and real-world scenarios. | 13657 | 2411.07763 |
Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition | https://openreview.net/forum?id=eHehzSDUFp | [
"Jiyeon Kim",
"Hyunji Lee",
"Hyowon Cho",
"Joel Jang",
"Hyeonbin Hwang",
"Seungpil Won",
"Youbin Ahn",
"Dohaeng Lee",
"Minjoon Seo"
] | Oral | In this work, we investigate how a model's tendency to broadly integrate its parametric knowledge evolves throughout pretraining, and how this behavior affects overall performance, particularly in terms of knowledge acquisition and forgetting. We introduce the concept of knowledge entropy, which quantifies the range of memory sources the model engages with; high knowledge entropy indicates that the model utilizes a wide range of memory sources, while low knowledge entropy suggests reliance on specific sources with greater certainty. Our analysis reveals a consistent decline in knowledge entropy as pretraining advances. We also find that the decline is closely associated with a reduction in the model's ability to acquire and retain knowledge, leading us to conclude that diminishing knowledge entropy (smaller number of active memory sources) impairs the model's knowledge acquisition and retention capabilities. We find further support for this by demonstrating that increasing the activity of inactive memory sources enhances the model's capacity for knowledge acquisition and retention. | knowledge entropy, knowledge acquisition and forgetting, evolving behavior during LLM pretraining | As pretraining progresses, models exhibit narrower integration of memory vectors, reflected by decreasing knowledge entropy, which hinders both knowledge acquisition and retention. | 13581 | 2410.01380 |
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance | https://openreview.net/forum?id=wM2sfVgMDH | [
"Yinan Zheng",
"Ruiming Liang",
"Kexin ZHENG",
"Jinliang Zheng",
"Liyuan Mao",
"Jianxiong Li",
"Weihao Gu",
"Rui Ai",
"Shengbo Eben Li",
"Xianyuan Zhan",
"Jingjing Liu"
] | Oral | Achieving human-like driving behaviors in complex open-world environments is a critical challenge in autonomous driving. Contemporary learning-based planning approaches such as imitation learning methods often struggle to balance competing objectives and lack of safety assurance,due to limited adaptability and inadequacy in learning complex multi-modal behaviors commonly exhibited in human planning, not to mention their strong reliance on the fallback strategy with predefined rules. We propose a novel transformer-based Diffusion Planner for closed-loop planning, which can effectively model multi-modal driving behavior and ensure trajectory quality without any rule-based refinement. Our model supports joint modeling of both prediction and planning tasks under the same architecture, enabling cooperative behaviors between vehicles. Moreover, by learning the gradient of the trajectory score function and employing a flexible classifier guidance mechanism, Diffusion Planner effectively achieves safe and adaptable planning behaviors. Evaluations on the large-scale real-world autonomous planning benchmark nuPlan and our newly collected 200-hour delivery-vehicle driving dataset demonstrate that Diffusion Planner achieves state-of-the-art closed-loop performance with robust transferability in diverse driving styles. | diffusion planning, autonomous driving | null | 13578 | 2501.15564 |
Learning to Search from Demonstration Sequences | https://openreview.net/forum?id=v593OaNePQ | [
"Dixant Mittal",
"Liwei Kang",
"Wee Sun Lee"
] | Oral | Search and planning are essential for solving many real-world problems. However, in numerous learning scenarios, only action-observation sequences, such as demonstrations or instruction sequences, are available for learning. Relying solely on supervised learning with these sequences can lead to sub-optimal performance due to the vast, unseen search space encountered during training. In this paper, we introduce Differentiable Tree Search Network (D-TSN), a novel neural network architecture that learns to construct search trees from just sequences of demonstrations by performing gradient descent on a best-first search tree construction algorithm. D-TSN enables the joint learning of submodules, including an encoder, value function, and world model, which are essential for planning. To construct the search tree, we employ a stochastic tree expansion policy and formulate it as another decision-making task. Then, we optimize the tree expansion policy via REINFORCE with an effective variance reduction technique for the gradient computation. D-TSN can be applied to problems with a known world model or to scenarios where it needs to jointly learn a world model with a latent state space. We study problems from these two scenarios, including Game of 24, 2D grid navigation, and Procgen games, to understand when D-TSN is more helpful. Through our experiments, we show that D-TSN is effective, especially when the world model with a latent state space is jointly learned. The code is available at https://github.com/dixantmittal/differentiable-tree-search-network. | planning, reasoning, learning to search, reinforcement learning, large language model | We propose a method that constructs search tree in a differetiable manner, and can be trained from just demonstration sequences. | 13425 | null |
Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse | https://openreview.net/forum?id=Iyrtb9EJBp | [
"Maojia Song",
"Shang Hong Sim",
"Rishabh Bhardwaj",
"Hai Leong Chieu",
"Navonil Majumder",
"Soujanya Poria"
] | Oral | LLMs are an integral component of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the overall quality of end-to-end RAG systems, there is a gap in understanding the appropriateness of LLMs for the RAG task. To address this, we introduce Trust-Score, a holistic metric that evaluates the trustworthiness of LLMs within the RAG framework. Our results show that various prompting methods, such as in-context learning, fail to effectively adapt LLMs to the RAG task as measured by Trust-Score. Consequently, we propose Trust-Align, a method to align LLMs for improved Trust-Score performance. 26 out of 27 models aligned using Trust-Align substantially outperform competitive baselines on ASQA, QAMPARI, and ELI5. Specifically, in LLaMA-3-8b, Trust-Align outperforms FRONT on ASQA (↑12.56), QAMPARI (↑36.04), and ELI5 (↑17.69). Trust-Align also significantly enhances models’ ability to correctly refuse and provide quality citations. We also demonstrate the effectiveness of Trust-Align across different open-weight models, including the LLaMA series (1b to 8b), Qwen-2.5 series (0.5b to 7b), and Phi3.5 (3.8b). We release our code at https://github.com/declare-lab/trust-align. | Large Language Models, Trustworthiness, Hallucinations, Retrieval Augmented Generation | How to better evaluate and make LLM better for RAG task | 13377 | 2409.11242 |
MAP: Multi-Human-Value Alignment Palette | https://openreview.net/forum?id=NN6QHwgRrQ | [
"Xinran Wang",
"Qi Le",
"Ammar Ahmed",
"Enmao Diao",
"Yi Zhou",
"Nathalie Baracaldo",
"Jie Ding",
"Ali Anwar"
] | Oral | Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the desirable levels of value alignment vary across different ethnic groups, industry sectors, and user cohorts. Within existing frameworks, it is hard to define human values and align AI systems accordingly across different directions simultaneously, such as harmlessness, helpfulness, and positiveness. To address this, we develop a novel, first-principle approach called Multi-Human-Value Alignment Palette (MAP), which navigates the alignment across multiple human values in a structured and reliable way. MAP formulates the alignment problem as an optimization task with user-defined constraints, which define human value targets. It can be efficiently solved via a primal-dual approach, which determines whether a user-defined alignment target is achievable and how to achieve it. We conduct a detailed theoretical analysis of MAP by quantifying the trade-offs between values, the sensitivity to constraints, the fundamental connection between multi-value alignment and sequential alignment, and proving that linear weighted rewards are sufficient for multi-value alignment. Extensive experiments demonstrate MAP's ability to align multiple values in a principled manner while delivering strong empirical performance across various tasks. | Human value alignment, Generative model | The paper introduces Multi-Human-Value Alignment Palette (MAP), a novel approach to align generative models with multiple human values in a principled way. | 13248 | 2410.19198 |
Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model | https://openreview.net/forum?id=is4nCVkSFA | [
"Siyu Chen",
"Beining Wu",
"Miao Lu",
"Zhuoran Yang",
"Tianhao Wang"
] | Oral | In this work, we tackle the following question: Can neural networks trained with gradient-based methods achieve the optimal statistical-computational tradeoff in learning Gaussian single-index models?
Prior research has shown that any polynomial-time algorithm under the statistical query (SQ) framework requires $\Omega(d^{s^\star/2}\lor d)$ samples, where $s^\star$ is the generative exponent representing the intrinsic difficulty of learning the underlying model.
However, it remains unknown whether neural networks can achieve this sample complexity.
Inspired by prior techniques such as label transformation and landscape smoothing for learning single-index models, we propose a unified gradient-based algorithm for training a two-layer neural network in polynomial time.
Our method is adaptable to a variety of loss and activation functions, covering a broad class of existing approaches.
We show that our algorithm learns a feature representation that strongly aligns with the unknown signal $\theta^\star$, with sample complexity $\tilde O (d^{s^\star/2} \lor d)$, matching the SQ lower bound up to a polylogarithmic factor for all generative exponents $s^\star\geq 1$.
Furthermore, we extend our approach to the setting where $\theta^\star$ is $k$-sparse for $k = o(\sqrt{d})$ by introducing a novel weight perturbation technique that leverages the sparsity structure.
We derive a corresponding SQ lower bound
of order $\tilde\Omega(k^{s^\star})$, matched by our method up to a polylogarithmic factor.
Our framework, especially the weight perturbation technique, is of independent interest, and suggests potential gradient-based solutions to other problems such as sparse tensor PCA. | single-index model, feature learning, gradient-based method, computational-statistical tradeoff | We propose a unified gradient-based algorithm for feature learning in Gaussian single-index model with sample complexity matching the SQ lower bound | 13084 | null |
Consistency Checks for Language Model Forecasters | https://openreview.net/forum?id=r5IXBlTCGc | [
"Daniel Paleka",
"Abhimanyu Pallavi Sudhir",
"Alejandro Alvarez",
"Vineeth Bhat",
"Adam Shen",
"Evan Wang",
"Florian Tramèr"
] | Oral | Forecasting is a task that is difficult to evaluate: the ground truth can only be known in the future. Recent work showing LLM forecasters rapidly approaching human-level performance begs the question: how can we benchmark and evaluate these forecasters *instantaneously*? Following the consistency check framework, we measure the performance of forecasters in terms of the consistency of their predictions on different logically-related questions. We propose a new, general consistency metric based on *arbitrage*: for example, if a forecasting AI illogically predicts that both the Democratic and Republican parties have 60\% probability of winning the 2024 US presidential election, an arbitrageur could trade against the forecaster's predictions and make a profit. We build an automated evaluation system that generates a set of base questions, instantiates consistency checks from these questions, elicits the predictions of the forecaster, and measures the consistency of the predictions. We then build a standard, proper-scoring-rule forecasting benchmark, and show that our (instantaneous) consistency metrics correlate strongly with LLM forecasters' ground truth Brier scores (which are only known in the future). We also release a consistency benchmark that resolves in 2028, providing a long-term evaluation tool for forecasting. | forecasting, markets, trading, LLM, evaluation, eval, consistency, robustness | It is difficult to evaluate AI forecasters instantaneously; we propose market-based consistency evals on LLM forecasters and show plenty of inconsistency. | 13065 | 2412.18544 |
Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment | https://openreview.net/forum?id=BPgK5XW1Nb | [
"Dongyoung Kim",
"Kimin Lee",
"Jinwoo Shin",
"Jaehyung Kim"
] | Oral | Aligning large language models (LLMs) with human preferences becomes a key component to obtaining state-of-the-art performance, but it yields a huge cost to construct a large human-annotated preference dataset. To tackle this problem, we propose a new framework, Spread Preference Annotation with direct preference judgment (SPA), that boosts the alignment of LLMs using only a very small amount of human-annotated preference data.
Our key idea is leveraging the human prior knowledge within the small (seed) data and progressively improving the alignment of LLM, by iteratively generating the responses and learning from them with the self-annotated preference data.
To be specific, we propose to derive the preference label from the logits of LLM to explicitly extract the model's inherent preference.
Compared to the previous approaches using external reward models or implicit in-context learning, we observe that the proposed approach is significantly more effective.
In addition, we introduce a noise-aware preference learning algorithm to mitigate the risk of low quality within generated preference data.
Our experimental results demonstrate that the proposed framework significantly boosts the alignment of LLMs.
For example, we achieve superior alignment performance on AlpacaEval 2.0 with only 3.3% of the ground-truth preference labels in the Ultrafeedback data compared to the cases using the entire data or state-of-the-art baselines. | large language model, alignment, preference | null | 12928 | 2406.04412 |
Brain Bandit: A Biologically Grounded Neural Network for Efficient Control of Exploration | https://openreview.net/forum?id=RWJX5F5I9g | [
"Chen Jiang",
"Jiahui An",
"Yating Liu",
"Ni Ji"
] | Oral | How to balance between exploration and exploitation in an uncertain environment is a central challenge in reinforcement learning. In contrast, humans and animals have demonstrated superior exploration efficiency in novel environments. To understand how the brain’s neural network controls exploration under uncertainty, we analyzed the dynamical systems model of a biological neural network that controls explore-exploit decisions during foraging. Mathematically, this model (named the Brain Bandit Net, or BBN) is a special type of stochastic continuous Hopfield network. We show through theory and simulation that BBN can perform posterior sampling of action values with a tunable bias towards or against uncertain options. We then demonstrate that, in multi-armed bandit (MAB) tasks, BBN can generate probabilistic choice behavior with a flexible uncertainty bias resembling human and animal choice patterns. In addition to its high efficiency in MAB tasks, BBN can also be embedded with reinforcement learning algorithms to accelerate learning in MDP tasks. Altogether, our findings reveal the theoretical foundation for efficient exploration in biological neural networks and propose a general, brain-inspired algorithm for enhancing exploration in RL. | explore-exploit, stochastic Hopfield network, Thompson sampling, decision under uncertainty, brain-inspired algorithm, reinforcement learning | We demonstrate that a brain-inspired stochastic Hopfield network can achieve efficient, human-like, uncertainty-aware exploration in bandit and MDP tasks. | 12774 | null |
MaestroMotif: Skill Design from Artificial Intelligence Feedback | https://openreview.net/forum?id=or8mMhmyRV | [
"Martin Klissarov",
"Mikael Henaff",
"Roberta Raileanu",
"Shagun Sodhani",
"Pascal Vincent",
"Amy Zhang",
"Pierre-Luc Bacon",
"Doina Precup",
"Marlos C. Machado",
"Pierluca D'Oro"
] | Oral | Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability. | Hierarchical RL, Reinforcement Learning, LLMs | A method for AI-assisted skill design via Motif and LLM code generation, solving tasks zero-shot from language descriptions on NetHack. | 12735 | 2412.08542 |
Learning to Discover Regulatory Elements for Gene Expression Prediction | https://openreview.net/forum?id=Mfnh1Sqdwf | [
"Xingyu Su",
"Haiyang Yu",
"Degui Zhi",
"Shuiwang Ji"
] | Oral | We consider the problem of predicting gene expressions from DNA sequences. A key challenge of this task is to find the regulatory elements that control gene expressions. Here, we introduce Seq2Exp, a Sequence to Expression network explicitly designed to discover and extract regulatory elements that drive target gene expression, enhancing the accuracy of the gene expression prediction. Our approach captures the causal relationship between epigenomic signals, DNA sequences and their associated regulatory elements. Specifically, we propose to decompose the epigenomic signals and the DNA sequence conditioned on the causal active regulatory elements, and apply an information bottleneck with the Beta distribution to combine their effects while filtering out non-causal components. Our experiments demonstrate that Seq2Exp outperforms existing baselines in gene expression prediction tasks and discovers influential regions compared to commonly used statistical methods for peak detection such as MACS3. The source code is released as part of the AIRS library (https://github.com/divelab/AIRS/). | Gene Expression, Deep Learning, Sequence Modeling | null | 12644 | 2502.13991 |
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models | https://openreview.net/forum?id=tyEyYT267x | [
"Marianne Arriola",
"Aaron Gokaslan",
"Justin T Chiu",
"Zhihan Yang",
"Zhixuan Qi",
"Jiaqi Han",
"Subham Sekhar Sahoo",
"Volodymyr Kuleshov"
] | Oral | Diffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeling and are limited to fixed-length generation. In this work, we introduce a class of block diffusion language models that interpolate between discrete denoising diffusion and autoregressive models. Block diffusion overcomes key limitations of both approaches by supporting flexible-length generation and improving inference efficiency with KV caching and parallel token sampling. We propose a recipe for building effective block diffusion models that includes an efficient training algorithm, estimators of gradient variance, and data-driven noise schedules to minimize the variance. Block diffusion sets a new state-of-the-art performance among diffusion models on language modeling benchmarks and enables generation of arbitrary-length sequences. We provide the code, along with the model weights and blog post on the project page: https://m-arriola.com/bd3lms/ | Diffusion Models, Text Diffusion, Generative Models | null | 12566 | 2503.09573 |
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo | https://openreview.net/forum?id=xoXn62FzD0 | [
"João Loula",
"Benjamin LeBrun",
"Li Du",
"Ben Lipkin",
"Clemente Pasti",
"Gabriel Grand",
"Tianyu Liu",
"Yahya Emara",
"Marjorie Freedman",
"Jason Eisner",
"Ryan Cotterell",
"Vikash Mansinghka",
"Alexander K. Lew",
"Tim Vieira",
"Timothy J. O'Donnell"
] | Oral | A wide range of LM applications require generating text that conforms to syntactic or semantic constraints. Imposing such constraints can be naturally framed as probabilistic conditioning, but exact generation from the resulting distribution—which can differ substantially from the LM’s base distribution—is generally intractable. In this work, we develop an architecture for controlled LM generation based on sequential Monte Carlo (SMC). This SMC framework allows us to flexibly incorporate domain- and problem-specific constraints at inference
time, and efficiently reallocate computational resources in light of new information during the course of generation. By comparing to a number of alternatives and ablations on four challenging domains—Python code generation for data science, text-to-SQL, goal inference, and molecule synthesis—we demonstrate that, with little overhead, our approach allows small open-source language models to outperform models over 8× larger, as well as closed-source, fine-tuned ones. In support of the probabilistic perspective, we show that these performance improvements are driven by better approximation to the posterior distribution. [Our system](https://github.com/probcomp/genparse) builds on the framework of Lew et al. (2023) and integrates with its language model probabilistic programming language, giving users a simple, programmable way to apply SMC to a broad variety of controlled generation problems. | Sequential Monte Carlo, Language Models, Semantic parsing, Bayesian inference, Probabilistic programming, SMC | We introduce a sequential Monte Carlo framework for controlling LMs at inference time via both syntactic and semantic constraints. | 12536 | null |
Scaling Laws for Precision | https://openreview.net/forum?id=wg1PCg3CUP | [
"Tanishq Kumar",
"Zachary Ankner",
"Benjamin Frederick Spector",
"Blake Bordelon",
"Niklas Muennighoff",
"Mansheej Paul",
"Cengiz Pehlevan",
"Christopher Re",
"Aditi Raghunathan"
] | Oral | Low precision training and inference affect both the quality and cost of language models, but current scaling laws do not account for this. In this work, we devise "precision-aware" scaling laws for both training and inference. We propose that training in lower precision reduces the model's "effective parameter count," allowing us to predict the additional loss incurred from training in low precision and post-train quantization. For inference, we find that the degradation introduced by post-training quantization increases as models are trained on more data, eventually making additional pretraining data actively harmful. For training, our scaling laws allow us to predict the loss of a model with different parts in different precisions, and suggest that training larger models in lower precision can be compute optimal. We unify the scaling laws for post and pretraining quantization to arrive at a single functional form that predicts degradation from training and inference in varied precisions. We fit on over 465 pretraining runs and validate our predictions on model sizes up to 1.7B parameters trained on up to 26B tokens. | quantization, scaling laws, precision, language models | We model the effects of precision on language model loss scaling, both during and after training. We find that overtrained models degrade more when quantized at inference time, and that training larger models in lower precision can be optimal. | 12529 | 2411.04330 |
Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance | https://openreview.net/forum?id=SPS6HzVzyt | [
"Sachin Goyal",
"Christina Baek",
"J Zico Kolter",
"Aditi Raghunathan"
] | Oral | Large Language Model's are instruction-finetuned to enhance their ability to follow user instructions and better comprehend input context. Still, they often struggle to follow the input context, especially when it contradicts model's parametric knowledge. This manifests as various failures, such as hallucinations where a model inserts outdated or unwarranted facts into its response. In this work, we observe an intriguing phenomenon: the context reliance of the model decreases as instruction finetuning progresses, $\textit{despite an initial expected increase}$. We call this phenomenon as the $\textbf{context-parametric inversion}$. This is surprising, as one would expect instruction tuning to improve the model's ability to follow input instructions. We observe this behavior on multiple general purpose instruction tuning datasets such as TULU, Alpaca and Ultrachat, across multiple model families like Llama, Mistral and Pythia. We perform various controlled studies to eliminate some simple hypothesis for this observed behavior and isolate what datapoints cause this counter-intuitive behavior. We then analyze the phenomenon theoretically, to explain why context reliance varies across the trajectory of finetuning.
We tie the observed context-parametric inversion to the properties of the finetuning data, which provides us with some potential mitigation strategies that provide limited but insightful gains. | Instruction finetuning, context-vs-parametric reliance | We highlight a surprising phenomenon, where the context reliance of the model decreases unexpectedly, with instruction finetuning, despite an initial increase. | 12499 | 2410.10796 |
Inference Scaling for Long-Context Retrieval Augmented Generation | https://openreview.net/forum?id=FSjIrOm1vz | [
"Zhenrui Yue",
"Honglei Zhuang",
"Aijun Bai",
"Kai Hui",
"Rolf Jagerman",
"Hansi Zeng",
"Zhen Qin",
"Dong Wang",
"Xuanhui Wang",
"Michael Bendersky"
] | Oral | The scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased compute is often allocated to incorporate more external knowledge. However, without effectively utilizing such knowledge, solely expanding context does not always enhance performance. In this work, we investigate inference scaling for retrieval augmented generation (RAG), exploring the combination of multiple strategies beyond simply increasing the quantity of knowledge, including in-context learning and iterative prompting. These strategies provide additional flexibility to scale test-time computation (e.g., by increasing retrieved documents or generation steps), thereby enhancing LLMs’ ability to effectively acquire and utilize contextual information. We address two key questions: (1) How does RAG performance benefit from the scaling of inference computation when optimally configured? (2) Can we predict the optimal test-time compute allocation for a given budget by modeling the relationship between RAG performance and inference parameters? Our observations reveal that increasing inference computation leads to nearly linear gains in RAG performance when optimally allocated, a relationship we describe as the inference scaling laws for RAG. Building on this, we further develop the computation allocation model to estimate RAG performance across different inference configurations. The model predicts optimal inference parameters under various computation constraints, which align closely with the experimental results. By applying these optimal configurations, we demonstrate that scaling inference compute on long-context LLMs achieves up to 58.9% gains on benchmark datasets compared to standard RAG. | inference scaling, long-context LLM, retrieval augmented generation | null | 12199 | 2410.04343 |
Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning | https://openreview.net/forum?id=4FWAwZtd2n | [
"Charlie Victor Snell",
"Jaehoon Lee",
"Kelvin Xu",
"Aviral Kumar"
] | Oral | Enabling LLMs to improve their outputs by using more test-time compute is a critical step towards building self-improving agents that can operate on open-ended natural language. In this paper, we scale up inference-time computation in LLMs, with a focus on answering: if an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a challenging prompt? Answering this question has implications not only on performance, but also on the future of LLM pretraining and how to tradeoff inference-time and pre-training compute. Little research has attempted to understand the scaling behaviors of test-time inference methods, with current work largely providing negative results for a number of these strategies. In this work, we analyze two primary mechanisms to scale test-time computation: (1) searching against dense, process-based verifier reward models (PRMs); and (2) updating the model's distribution over a response adaptively, given the prompt at test time. We find that in both cases, the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt. This observation motivates applying a "compute-optimal" scaling strategy, which acts to, as effectively as possible, allocate test-time compute per prompt in an adaptive manner. Using this compute-optimal strategy, we can improve the efficiency of test-time compute scaling for math reasoning problems by more than 4x compared to a best-of-N baseline. Additionally, in a FLOPs-matched evaluation, we find that on problems where a smaller base model attains somewhat non-trivial success rates, test-time compute can be used to outperform a 14x larger model. | test-time compute, LLMs, scaling, language models | We find that by optimally scaling test-time compute we can outperform much larger models in a FLOPs matched evaluation. | 12182 | null |
Capturing the Temporal Dependence of Training Data Influence | https://openreview.net/forum?id=uHLgDEgiS5 | [
"Jiachen T. Wang",
"Dawn Song",
"James Zou",
"Prateek Mittal",
"Ruoxi Jia"
] | Oral | Traditional data influence estimation methods, like influence function, assume that learning algorithms are permutation-invariant with respect to training data. However, modern training paradigms—especially for foundation models using stochastic algorithms and non-convergent, multi-stage curricula—are sensitive to data ordering, thus violating this assumption. This mismatch renders influence functions inadequate for answering some critical questions in current machine learning: How can we differentiate the influence of the same data contributing at different stages of training? More generally, how can we capture the dependence of data influence on the optimization trajectory during training? To address this gap, we formalize the concept of \emph{trajectory-specific leave-one-out (LOO) influence}, which quantifies the impact of removing a data point from a specific iteration during training, accounting for the exact sequence of data encountered and the model's optimization trajectory. However, exactly evaluating the trajectory-specific LOO presents a significant computational challenge. To address this, we propose \emph{data value embedding}, a novel technique enabling efficient approximation of trajectory-specific LOO. Specifically, we compute a training data embedding that encapsulates the cumulative interactions between data and the evolving model parameters. The LOO can then be efficiently approximated through a simple dot-product between the data value embedding and the gradient of the given test data. As data value embedding captures training data ordering, it offers valuable insights into model training dynamics. In particular, we uncover distinct phases of data influence, revealing that data points in the early and late stages of training exert a greater impact on the final model. These insights translate into actionable strategies for managing the computational overhead of data selection by strategically timing the selection process, potentially opening new avenues in data curation research. | data attribution | We introduce data value embedding, a novel framework for real-time data attribution technique that approximates trajectory-specific leave-one-out (LOO) error. | 12172 | 2412.09538 |
Self-Improvement in Language Models: The Sharpening Mechanism | https://openreview.net/forum?id=WJaUkwci9o | [
"Audrey Huang",
"Adam Block",
"Dylan J Foster",
"Dhruv Rohatgi",
"Cyril Zhang",
"Max Simchowitz",
"Jordan T. Ash",
"Akshay Krishnamurthy"
] | Oral | Recent work in language modeling has raised the possibility of “self-improvement,” where an LLM evaluates and refines its own generations to achieve higher performance without external feedback. It is impossible for this self-improvement to create information that is not already in the model, so why should we expect that this will lead to improved capabilities? We offer a new theoretical perspective on the capabilities of self-improvement through a lens we refer to as “sharpening.” Motivated by the observation that language models are often better at verifying response quality than they are at generating correct responses, we formalize self-improvement as using the model itself as a verifier during post-training in order to ‘sharpen’ the model to one placing large mass on high-quality sequences, thereby amortizing the expensive inference-time computation of generating good sequences. We begin by introducing a new statistical framework for sharpening in which the learner has sample access to a pre-trained base policy. Then, we analyze two natural families of self improvement algorithms based on SFT and RLHF. We find that (i) the SFT-based approach is minimax optimal whenever the initial model has sufficient coverage, but (ii) the RLHF-based approach can improve over SFT-based self- improvement by leveraging online exploration, bypassing the need for coverage. We view these findings as a starting point toward a foundational understanding that can guide the design and evaluation of self-improvement algorithms. | Learning theory, Sample complexity, Self-Improvement, Language Models | We offer a new theoretical perspective on the possibility of self-improvement in language models. | 12101 | 2412.01951 |
Data Shapley in One Training Run | https://openreview.net/forum?id=HD6bWcj87Y | [
"Jiachen T. Wang",
"Prateek Mittal",
"Dawn Song",
"Ruoxi Jia"
] | Oral | Data Shapley offers a principled framework for attributing the contribution of data within machine learning contexts. However, the traditional notion of Data Shapley requires re-training models on various data subsets, which becomes computationally infeasible for large-scale models. Additionally, this retraining-based definition cannot evaluate the contribution of data for a specific model training run, which may often be of interest in practice. This paper introduces a novel concept, In-Run Data Shapley, which eliminates the need for model retraining and is specifically designed for assessing data contribution for a particular model of interest. In-Run Data Shapley calculates the Shapley value for each gradient update iteration and accumulates these values throughout the training process. We present several techniques that allow the efficient scaling of In-Run Data Shapley to the size of foundation models. In its most optimized implementation, our method adds negligible runtime overhead compared to standard model training. This dramatic efficiency improvement makes it possible to perform data attribution for the foundation model pretraining stage. We present several case studies that offer fresh insights into pretraining data's contribution and discuss their implications for copyright in generative AI and pretraining data curation. | Shapley value, data valuation. | We develop a new notion of Data Shapley that requires only one model training run. | 12092 | 2406.11011 |
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics | https://openreview.net/forum?id=hyfe5q5TD0 | [
"Runzhe Wu",
"Ayush Sekhari",
"Akshay Krishnamurthy",
"Wen Sun"
] | Oral | We study computationally and statistically efficient Reinforcement Learning algorithms for the *linear Bellman Complete* setting. This setting uses linear function approximation to capture value functions and unifies existing models like linear Markov Decision Processes (MDP) and Linear Quadratic Regulators (LQR). While it is known from the prior works that this setting is statistically tractable, it remained open whether a computationally efficient algorithm exists. Our work provides a computationally efficient algorithm for the linear Bellman complete setting that works for MDPs with large action spaces, random initial states, and random rewards but relies on the underlying dynamics to be deterministic. Our approach is based on randomization: we inject random noise into least squares regression problems to perform optimistic value iteration. Our key technical contribution is to carefully design the noise to only act in the null space of the training data to ensure optimism while circumventing a subtle error amplification issue. | reinforcement learning theory, linear function approximation | null | 12025 | 2406.11810 |
Linear Representations of Political Perspective Emerge in Large Language Models | https://openreview.net/forum?id=rwqShzb9li | [
"Junsol Kim",
"James Evans",
"Aaron Schein"
] | Oral | Large language models (LLMs) have demonstrated the ability to generate text that realistically reflects a range of different subjective human perspectives. This paper studies how LLMs are seemingly able to reflect more liberal versus more conservative viewpoints among other political perspectives in American politics. We show that LLMs possess linear representations of political perspectives within activation space, wherein more similar perspectives are represented closer together. To do so, we probe the attention heads across the layers of three open transformer-based LLMs (\texttt{Llama-2-7b-chat}, \texttt{Mistral-7b-instruct}, \texttt{Vicuna-7b}). We first prompt models to generate text from the perspectives of different U.S.~lawmakers. We then identify sets of attention heads whose activations linearly predict those lawmakers' DW-NOMINATE scores, a widely-used and validated measure of political ideology. We find that highly predictive heads are primarily located in the middle layers, often speculated to encode high-level concepts and tasks. Using probes only trained to predict lawmakers' ideology, we then show that the same probes can predict measures of news outlets' slant from the activations of models prompted to simulate text from those news outlets. These linear probes allow us to visualize, interpret, and monitor ideological stances implicitly adopted by an LLM as it generates open-ended responses. Finally, we demonstrate that by applying linear interventions to these attention heads, we can steer the model outputs toward a more liberal or conservative stance. Overall, our research suggests that LLMs possess a high-level linear representation of American political ideology and that by leveraging recent advances in mechanistic interpretability, we can identify, monitor, and steer the subjective perspective underlying generated text. | large language model, political perspective, ideology, representation learning | LLMs possess linear representations of political perspectives (left-right) within the activation space. By applying linear interventions to the activation space, we can steer the model's outputs toward a more liberal or conservative stance. | 11965 | 2503.02080 |
Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs | https://openreview.net/forum?id=FBkpCyujtS | [
"Nguyen Nhat Minh",
"Andrew Baker",
"Clement Neo",
"Allen G Roush",
"Andreas Kirsch",
"Ravid Shwartz-Ziv"
] | Oral | Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. Popular sampling methods like top-p (nucleus sampling) often struggle to balance quality and diversity, especially at higher temperatures which lead to incoherent or repetitive outputs. We propose min-p sampling, a dynamic truncation method that adjusts the sampling threshold based on the model's confidence by using the top token's probability as a scaling factor. Our experiments on benchmarks including GPQA, GSM8K, and AlpacaEval Creative Writing show that min-p sampling improves both the quality and diversity of generated text across different model families (Mistral and Llama 3) and model sizes (1B to 123B parameters), especially at higher temperatures. Human evaluations further show a clear preference for min-p sampling, in both text quality and creativity. Min-p sampling has been adopted by popular open-source LLM frameworks, including Hugging Face Transformers, VLLM, and many others, highlighting its significant impact on improving text generation quality. | Natural Language Processing, Large Language Models, Text Generation, Sampling Methods, Truncation Sampling, Stochastic Sampling, Min-p Sampling, Top-p Sampling, Nucleus Sampling, Temperature Sampling, Decoding Methods, Deep Learning, Artificial Intelligence | Min-p sampling, a dynamic truncation sampler for LLMs, improves text quality and diversity, especially at higher temperature settings. | 11935 | null |
Joint Graph Rewiring and Feature Denoising via Spectral Resonance | https://openreview.net/forum?id=zBbZ2vdLzH | [
"Jonas Linkerhägner",
"Cheng Shi",
"Ivan Dokmanić"
] | Oral | When learning from graph data, the graph and the node features both give noisy information about the node labels. In this paper we propose an algorithm to **j**ointly **d**enoise the features and **r**ewire the graph (JDR), which improves the performance of downstream node classification graph neural nets (GNNs). JDR works by aligning the leading spectral spaces of graph and feature matrices. It approximately solves the associated non-convex optimization problem in a way that handles graphs with multiple classes and different levels of homophily or heterophily. We theoretically justify JDR in a stylized setting and show that it consistently outperforms existing rewiring methods on a wide range of synthetic and real-world node classification tasks. | GNNs, Rewiring, Denoising, Spectral Resonance, cSBM | We introduce joint denoising and rewiring (JDR)—an algorithm to jointly rewire the graph and denoise the features, which improves the performance of downstream node classification GNNs. | 11879 | 2408.07191 |
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning | https://openreview.net/forum?id=Pujt3ADZgI | [
"Yuheng Zhang",
"Dian Yu",
"Baolin Peng",
"Linfeng Song",
"Ye Tian",
"Mingyue Huo",
"Nan Jiang",
"Haitao Mi",
"Dong Yu"
] | Oral | Reinforcement Learning with Human Feedback (RLHF) has achieved great success
in aligning large language models (LLMs) with human preferences. Prevalent
RLHF approaches are reward-based, following the Bradley-Terry (BT) model assumption, which may not fully capture the complexity of human preferences. In
this paper, we explore RLHF under a general preference framework and approach
it from a game-theoretic perspective. Specifically, we formulate the problem as
a two-player game and propose a novel online algorithm, iterative Nash policy
optimization (INPO). The key idea is to let the policy play against itself via no-
regret learning, thereby approximating the Nash policy. Unlike previous methods,
INPO bypasses the need for estimating the expected win rate for individual responses, which typically incurs high computational or annotation costs. Instead,
we introduce a new loss objective that is directly minimized over a preference
dataset. We provide theoretical analysis for our approach and demonstrate its
effectiveness through experiments on various representative benchmarks. With an
LLaMA-3-8B-based SFT model, INPO achieves a 42.6% length-controlled win
rate on AlpacaEval 2.0 and a 37.8% win rate on Arena-Hard, showing substantial
improvement over the state-of-the-art online RLHF algorithms. | RLHF Theory, LLM Alignment | null | 11848 | 2407.00617 |
Progressive Compression with Universally Quantized Diffusion Models | https://openreview.net/forum?id=CxXGvKRDnL | [
"Yibo Yang",
"Justus Will",
"Stephan Mandt"
] | Oral | Diffusion probabilistic models have achieved mainstream success in many generative modeling tasks, from image generation to inverse problem solving. A distinct feature of these models is that they correspond to deep hierarchical latent variable models optimizing a variational evidence lower bound (ELBO) on the data likelihood. Drawing on a basic connection between likelihood modeling and compression, we explore the potential of diffusion models for progressive coding, resulting in a sequence of bits that can be incrementally transmitted and decoded with progressively improving reconstruction quality. Unlike prior work based on Gaussian diffusion or conditional diffusion models, we propose a new form of diffusion model with uniform noise in the forward process, whose negative ELBO corresponds to the end-to-end compression cost using universal quantization. We obtain promising first results on image compression, achieving competitive rate-distortion-realism results on a wide range of bit-rates with a single model, bringing neural codecs a step closer to practical deployment. Our code can be found at https://github.com/mandt-lab/uqdm. | diffusion, generative modeling, compression, universal quantization | We improve practical compression with an unconditional diffusion model, proposing a new form of diffusion based on uniform noise instead of Gaussian noise. | 11617 | 2412.10935 |
Accelerated training through iterative gradient propagation along the residual path | https://openreview.net/forum?id=JDm7oIcx4Y | [
"Erwan Fagnou",
"Paul Caillon",
"Blaise Delattre",
"Alexandre Allauzen"
] | Oral | Despite being the cornerstone of deep learning, backpropagation is criticized for its inherent sequentiality, which can limit the scalability of very deep models.
Such models faced convergence issues due to vanishing gradient, later resolved using residual connections. Variants of these are now widely used in modern architectures.
However, the computational cost of backpropagation remains a major burden, accounting for most of the training time.
Taking advantage of residual-like architectural designs, we introduce Highway backpropagation, a parallelizable iterative algorithm that approximates backpropagation, by alternatively i) accumulating the gradient estimates along the residual path, and ii) backpropagating them through every layer in parallel. This algorithm is naturally derived from a decomposition of the gradient as the sum of gradients flowing through all paths, and is adaptable to a diverse set of common architectures, ranging from ResNets and Transformers to recurrent neural networks.
Through an extensive empirical study on a large selection of tasks and models, we evaluate Highway-BP and show that major speedups can be achieved with minimal performance degradation. | optimization, efficient training | We propose Highway backpropagation, a parallelizable algorithm that accelerates training by leveraging residual connections, achieving significant speedups with minimal performance loss across various architectures. | 11584 | 2501.17086 |
Tight Lower Bounds under Asymmetric High-Order Hölder Smoothness and Uniform Convexity | https://openreview.net/forum?id=fMTPkDEhLQ | [
"Site Bai",
"Brian Bullins"
] | Oral | In this paper, we provide tight lower bounds for the oracle complexity of minimizing high-order Hölder smooth and uniformly convex functions. Specifically, for a function whose $p^{th}$-order derivatives are Hölder continuous with degree $\nu$ and parameter $H$, and that is uniformly convex with degree $q$ and parameter $\sigma$, we focus on two asymmetric cases: (1) $q > p + \nu$, and (2) $q < p+\nu$. Given up to $p^{th}$-order oracle access, we establish worst-case oracle complexities of $\Omega\left( \left( \frac{H}{\sigma}\right)^\frac{2}{3(p+\nu)-2}\left( \frac{\sigma}{\epsilon}\right)^\frac{2(q-p-\nu)}{q(3(p+\nu)-2)}\right)$ in the first case with an $\ell_\infty$-ball-truncated-Gaussian smoothed hard function and $\Omega\left(\left(\frac{H}{\sigma}\right)^\frac{2}{3(p+\nu)-2}+ \log\log\left(\left(\frac{\sigma^{p+\nu}}{H^q}\right)^\frac{1}{p+\nu-q}\frac{1}{\epsilon}\right)\right)$ in the second case, for reaching an $\epsilon$-approximate solution in terms of the optimality gap. Our analysis generalizes previous lower bounds for functions under first- and second-order smoothness as well as those for uniformly convex functions, and furthermore our results match the corresponding upper bounds in this general setting. | Convex Optimization, Uniform Convexity, Lower Bound, High-Order Method, Regularization, Hölder Smoothness | This paper establishes tight lower bounds for minimizing high-order Hölder smooth and uniformly convex functions with high-order oracle access. | 11481 | null |
ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design | https://openreview.net/forum?id=KSLkFYHlYg | [
"Keir Adams",
"Kento Abeywardane",
"Jenna Fromer",
"Connor W. Coley"
] | Oral | Engineering molecules to exhibit precise 3D intermolecular interactions with their environment forms the basis of chemical design. In ligand-based drug design, bioisosteric analogues of known bioactive hits are often identified by virtually screening chemical libraries with shape, electrostatic, and pharmacophore similarity scoring functions. We instead hypothesize that a generative model which learns the joint distribution over 3D molecular structures and their interaction profiles may facilitate 3D interaction-aware chemical design. We specifically design ShEPhERD, an SE(3)-equivariant diffusion model which jointly diffuses/denoises 3D molecular graphs and representations of their shapes, electrostatic potential surfaces, and (directional) pharmacophores to/from Gaussian noise. Inspired by traditional ligand discovery, we compose 3D similarity scoring functions to assess ShEPhERD’s ability to conditionally generate novel molecules with desired interaction profiles. We demonstrate ShEPhERD’s potential for impact via exemplary drug design tasks including natural product ligand hopping, protein-blind bioactive hit diversification, and bioisosteric fragment merging. | 3D molecular generation, drug design, molecules | We design a diffusion model that jointly generates 3D molecules and explicit representations of their 3D shapes, electrostatics, and pharmacophores and demonstrate its utility in bioisosteric drug design | 11461 | 2411.04130 |
Restructuring Vector Quantization with the Rotation Trick | https://openreview.net/forum?id=GMwRl2e9Y1 | [
"Christopher Fifty",
"Ronald Guenther Junkins",
"Dennis Duan",
"Aniketh Iyengar",
"Jerry Weihong Liu",
"Ehsan Amid",
"Sebastian Thrun",
"Christopher Re"
] | Oral | Vector Quantized Variational AutoEncoders (VQ-VAEs) are designed to compress a continuous input to a discrete latent space and reconstruct it with minimal distortion.
They operate by maintaining a set of vectors---often referred to as the codebook---and quantizing each encoder output to the nearest vector in the codebook.
However, as vector quantization is non-differentiable, the gradient to the encoder flows _around_ the vector quantization layer rather than _through_ it in a straight-through approximation.
This approximation may be undesirable as all information from the vector quantization operation is lost.
In this work, we propose a way to propagate gradients through the vector quantization layer of VQ-VAEs.
We smoothly transform each encoder output into its corresponding codebook vector via a rotation and rescaling linear transformation that is treated as a constant during backpropagation.
As a result, the relative magnitude and angle between encoder output and codebook vector becomes encoded into the gradient as it propagates through the vector quantization layer and back to the encoder.
Across 11 different VQ-VAE training paradigms, we find this restructuring improves reconstruction metrics, codebook utilization, and quantization error. | Vector Quantization, VQ-VAE | null | 11319 | 2410.06424 |
Interpreting Emergent Planning in Model-Free Reinforcement Learning | https://openreview.net/forum?id=DzGe40glxs | [
"Thomas Bush",
"Stephen Chung",
"Usman Anwar",
"Adrià Garriga-Alonso",
"David Krueger"
] | Oral | We present the first mechanistic evidence that model-free reinforcement learning agents can learn to plan. This is achieved by applying a methodology based on concept-based interpretability to a model-free agent in Sokoban -- a commonly used benchmark for studying planning. Specifically, we demonstrate that DRC, a generic model-free agent introduced by [Guez et al. (2019)](https://arxiv.org/abs/1901.03559), uses learned concept representations to internally formulate plans that both predict the long-term effects of actions on the environment and influence action selection. Our methodology involves: (1) probing for planning-relevant concepts, (2) investigating plan formation within the agent's representations, and (3) verifying that discovered plans (in the agent's representations) have a causal effect on the agent's behavior through interventions. We also show that the emergence of these plans coincides with the emergence of a planning-like property: the ability to benefit from additional test-time compute. Finally, we perform a qualitative analysis of the planning algorithm learned by the agent and discover a strong resemblance to parallelized bidirectional search. Our findings advance understanding of the internal mechanisms underlying planning behavior in agents, which is important given the recent trend of emergent planning and reasoning capabilities in LLMs through RL. | reinforcement learning, interpretability, planning, probes, model-free, mechanistic interpretability, sokoban | We introduce and utilise a concept-based methodology to provide the first non-behavioural evidence that model-free agents can learn to plan | 11267 | null |
Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization | https://openreview.net/forum?id=kX8h23UG6v | [
"Zhitong Xu",
"Haitao Wang",
"Jeff M. Phillips",
"Shandian Zhe"
] | Oral | A long-standing belief holds that Bayesian Optimization (BO) with standard Gaussian processes (GP) --- referred to as standard BO --- underperforms in high-dimensional optimization problems. While this belief seems plausible, it lacks both robust empirical evidence and theoretical justification. To address this gap, we present a systematic investigation. First, through a comprehensive evaluation across twelve benchmarks, we found that while the popular Square Exponential (SE) kernel often leads to poor performance, using Mat\'ern kernels enables standard BO to consistently achieve top-tier results, frequently surpassing methods specifically designed for high-dimensional optimization. Second, our theoretical analysis reveals that the SE kernel’s failure primarily stems from improper initialization of the length-scale parameters, which are commonly used in practice but can cause gradient vanishing in training. We provide a probabilistic bound to characterize this issue, showing that Mat\'ern kernels are less susceptible and can robustly handle much higher dimensions. Third, we propose a simple robust initialization strategy that dramatically improves the performance of the SE kernel, bringing it close to state-of-the-art methods, without requiring additional priors or regularization. We prove another probabilistic bound that demonstrates how the gradient vanishing issue can be effectively mitigated with our method. Our findings advocate for a re-evaluation of standard BO’s potential in high-dimensional settings. | Gaussian Process, Bayesian Optimization, High Dimensional Bayesian Optimization | We identified a critical failure mode of standard Bayesian Optimization in high-dimensional optimization and proposed a simple yet effective solution | 11190 | 2402.02746 |
Limits to scalable evaluation at the frontier: LLM as judge won’t beat twice the data | https://openreview.net/forum?id=NO6Tv6QcDs | [
"Florian E. Dorner",
"Vivian Yvonne Nastl",
"Moritz Hardt"
] | Oral | High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem. Scalable evaluation methods that avoid costly annotation have therefore become an important research ambition. Many hope to use strong existing models in lieu of costly labels to provide cheap model evaluations. Unfortunately, this method of using models as judges introduces biases, such as self-preferencing, that can distort model comparisons. An emerging family of debiasing tools promises to fix these issues by using a few high quality labels to debias a large number of model judgments. In this paper, we study how far such debiasing methods, in principle, can go. Our main result shows that when the judge is no more accurate than the evaluated model, no debiasing method can decrease the required amount of ground truth labels by more than half. Our result speaks to the severe limitations of the LLM-as-a-judge paradigm at the evaluation frontier where the goal is to assess newly released models that are possibly better than the judge. Through an empirical evaluation, we demonstrate that the sample size savings achievable in practice are even more modest than what our theoretical limit suggests. Along the way, our work provides new observations about debiasing methods for model evaluation, and points out promising avenues for future work. | Evaluation, Benchmarking, Model-as-a-judge, Theory | null | 11163 | null |
DEPT: Decoupled Embeddings for Pre-training Language Models | https://openreview.net/forum?id=vf5aUZT0Fz | [
"Alex Iacob",
"Lorenzo Sani",
"Meghdad Kurmanji",
"William F. Shen",
"Xinchi Qiu",
"Dongqi Cai",
"Yan Gao",
"Nicholas Donald Lane"
] | Oral | Language Model pre-training uses broad data mixtures to enhance performance across domains and languages. However, training on such heterogeneous text corpora requires extensive and expensive efforts. Since these data sources vary significantly in lexical, syntactic, and semantic aspects, they cause negative interference or the ``curse of multilinguality''. To address these challenges we propose a communication-efficient pre-training framework, DEPT. Our method decouples embeddings from the transformer body while simultaneously training the latter on multiple data sources without requiring a shared vocabulary. DEPT can: (1) train robustly and effectively under significant data heterogeneity, (2) minimize token embedding parameters to only what the data source vocabulary requires, while cutting communication costs in direct proportion to both the communication frequency and the reduction in parameters, (3) enhance transformer body plasticity and generalization, improving both average perplexity (up to 20%) and downstream task performance, and (4) enable training with custom optimized vocabularies per data source. We demonstrate DEPT's potential via the first vocabulary-agnostic federated pre-training of billion-scale models, reducing communication costs by orders of magnitude and embedding memory by 4-5x. | Decentralized Training, Federated Learning, Multi-domain Training, Multilingual Training | We propose DEPT, a pre-training framework that decouples embedding layers from the transformer body, enabling robust training on heterogeneous data, improving generalization, and reducing memory footprint. | 11135 | 2410.05021 |
Homomorphism Expressivity of Spectral Invariant Graph Neural Networks | https://openreview.net/forum?id=rdv6yeMFpn | [
"Jingchu Gai",
"Yiheng Du",
"Bohang Zhang",
"Haggai Maron",
"Liwei Wang"
] | Oral | Graph spectra are an important class of structural features on graphs that have shown promising results in enhancing Graph Neural Networks (GNNs). Despite their widespread practical use, the theoretical understanding of the power of spectral invariants --- particularly their contribution to GNNs --- remains incomplete. In this paper, we address this fundamental question through the lens of homomorphism expressivity, providing a comprehensive and quantitative analysis of the expressive power of spectral invariants. Specifically, we prove that spectral invariant GNNs can homomorphism-count exactly a class of specific tree-like graphs which we refer to as \emph{parallel trees}. We highlight the significance of this result in various contexts, including establishing a quantitative expressiveness hierarchy across different architectural variants, offering insights into the impact of GNN depth, and understanding the subgraph counting capabilities of spectral invariant GNNs. In particular, our results significantly extend \citet{arvind2024hierarchy} and settle their open questions. Finally, we generalize our analysis to higher-order GNNs and answer an open question raised by \citet{zhang2024expressive}. | Graph Neural Network, Expressive Power, Spectral Invariant, Graph Homomorphism, Weisfeiler-Lehman | We analyze the expressive power of spectral invariant graph neural networks from the perspective of graph homomorphisms. | 11126 | 2503.00485 |
RB-Modulation: Training-Free Stylization using Reference-Based Modulation | https://openreview.net/forum?id=bnINPG5A32 | [
"Litu Rout",
"Yujia Chen",
"Nataniel Ruiz",
"Abhishek Kumar",
"Constantine Caramanis",
"Sanjay Shakkottai",
"Wen-Sheng Chu"
] | Oral | We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models.
Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content.
RB-Modulation is built on a novel stochastic optimal controller where a style descriptor encodes the desired attributes through a terminal cost.
The resulting drift not only overcomes the difficulties above, but also ensures high fidelity to the reference style and adheres to the given text prompt.
We also introduce a cross-attention-based feature aggregation scheme that allows RB-Modulation to decouple content and style from the reference image.
With theoretical justification and empirical evidence, our test-time optimization framework demonstrates precise extraction and control of *content* and *style* in a training-free manner.
Further, our method allows a seamless composition of content and style, which marks a departure from the dependency on external adapters or ControlNets. See project page: https://rb-modulation.github.io/ for code and further details. | Inverse Problems, Generative Modeling, Diffusion Models, Posterior Sampling, Optimal Control, Test-time Optimization | We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play test-time optimization method for training-free personalization (stylization and content-style composition) of diffusion models using stochastic optimal control. | 11075 | null |
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks | https://openreview.net/forum?id=zCxGCdzreM | [
"Michael Matthews",
"Michael Beukman",
"Chris Lu",
"Jakob Nicolaus Foerster"
] | Oral | While large models trained with self-supervised learning on offline datasets have shown remarkable capabilities in text and image domains, achieving the same generalisation for agents that act in sequential decision problems remains an open challenge.
In this work, we take a step towards this goal by procedurally generating tens of millions of 2D physics-based tasks and using these to train a general reinforcement learning (RL) agent for physical control.
To this end, we introduce Kinetix: an open-ended space of physics-based RL environments that can represent tasks ranging from robotic locomotion and grasping to video games and classic RL environments, all within a unified framework.
Kinetix makes use of our novel hardware-accelerated physics engine Jax2D that allows us to cheaply simulate billions of environment steps during training.
Our trained agent exhibits strong physical reasoning capabilities in 2D space, being able to zero-shot solve unseen human-designed environments. Furthermore, fine-tuning this general agent on tasks of interest shows significantly stronger performance than training an RL agent *tabula rasa*. This includes solving some environments that standard RL training completely fails at.
We believe this demonstrates the feasibility of large scale, mixed-quality pre-training for online RL and we hope that Kinetix will serve as a useful framework to investigate this further. | reinforcement learning, open-endedness, unsupervised environment design, automatic curriculum learning, benchmark | Training with reinforcement learning on a vast open-ended distribution of physics-based tasks leads to an agent that can zero-shot solve human-designed problems. | 10946 | 2410.23208 |
OptionZero: Planning with Learned Options | https://openreview.net/forum?id=3IFRygQKGL | [
"Po-Wei Huang",
"Pei-Chiun Peng",
"Hung Guei",
"Ti-Rong Wu"
] | Oral | Planning with options -- a sequence of primitive actions -- has been shown effective in reinforcement learning within complex environments. Previous studies have focused on planning with predefined options or learned options through expert demonstration data.
Inspired by MuZero, which learns superhuman heuristics without any human knowledge, we propose a novel approach, named *OptionZero*. OptionZero incorporates an *option network* into MuZero, providing autonomous discovery of options through self-play games. Furthermore, we modify the dynamics network to provide environment transitions when using options, allowing searching deeper under the same simulation constraints. Empirical experiments conducted in 26 Atari games demonstrate that OptionZero outperforms MuZero, achieving a 131.58% improvement in mean human-normalized score. Our behavior analysis shows that OptionZero not only learns options but also acquires strategic skills tailored to different game characteristics. Our findings show promising directions for discovering and using options in planning. Our code is available at https://rlg.iis.sinica.edu.tw/papers/optionzero. | Option, Semi-MDP, MuZero, MCTS, Planning, Reinforcement Learning | This paper presents OptionZero, a method that integrates options into the MuZero algorithm, which autonomously discovers options through self-play games and utilizes options during planning. | 10733 | 2502.16634 |
Instant Policy: In-Context Imitation Learning via Graph Diffusion | https://openreview.net/forum?id=je3GZissZc | [
"Vitalis Vosylius",
"Edward Johns"
] | Oral | Following the impressive capabilities of in-context learning with large transformers, In-Context Imitation Learning (ICIL) is a promising opportunity for robotics. We introduce Instant Policy, which learns new tasks instantly from just one or two demonstrations, achieving ICIL through two key components. First, we introduce inductive biases through a graph representation and model ICIL as a graph generation problem using a learned diffusion process, enabling structured reasoning over demonstrations, observations, and actions. Second, we show that such a model can be trained using pseudo-demonstrations – arbitrary trajectories generated in simulation – as a virtually infinite pool of training data. Our experiments, in both simulation and reality, show that Instant Policy enables rapid learning of various everyday robot tasks. We also show how it can serve as a foundation for cross-embodiment and zero-shot transfer to language-defined tasks. | In-context Imitation Learning, Robotic Manipulation, Graph Neural Networks, Diffusion Models | We formulate In-Context Imitation Learning as a diffusion-based graph generation problem and learn it using procedurally generated pseudo-demonstrations. | 10684 | 2411.12633 |
What should a neuron aim for? Designing local objective functions based on information theory | https://openreview.net/forum?id=CLE09ESvul | [
"Andreas Christian Schneider",
"Valentin Neuhaus",
"David Alexander Ehrlich",
"Abdullah Makkeh",
"Alexander S Ecker",
"Viola Priesemann",
"Michael Wibral"
] | Oral | In modern deep neural networks, the learning dynamics of individual neurons are often obscure, as the networks are trained via global optimization. Conversely, biological systems build on self-organized, local learning, achieving robustness and efficiency with limited global information. Here, we show how self-organization between individual artificial neurons can be achieved by designing abstract bio-inspired local learning goals. These goals are parameterized using a recent extension of information theory, Partial Information Decomposition (PID), which decomposes the information that a set of information sources holds about an outcome into unique, redundant and synergistic contributions. Our framework enables neurons to locally shape the integration of information from various input classes, i.e., feedforward, feedback, and lateral, by selecting which of the three inputs should contribute uniquely, redundantly or synergistically to the output. This selection is expressed as a weighted sum of PID terms, which, for a given problem, can be directly derived from intuitive reasoning or via numerical optimization, offering a window into understanding task-relevant local information processing. Achieving neuron-level interpretability while enabling strong performance using local learning, our work advances a principled information-theoretic foundation for local learning strategies. | local learning, interpretability, neuro-inspired, information theory, partial information decomposition | This paper proposes using Partial Information Decomposition as a local objective for neurons to improve neuron-level interpretability. | 10601 | 2412.02482 |
Cross-Entropy Is All You Need To Invert the Data Generating Process | https://openreview.net/forum?id=hrqNOxpItr | [
"Patrik Reizinger",
"Alice Bizeul",
"Attila Juhos",
"Julia E Vogt",
"Randall Balestriero",
"Wieland Brendel",
"David Klindt"
] | Oral | Supervised learning has become a cornerstone of modern machine learning, yet a comprehensive theory explaining its effectiveness remains elusive. Empirical phenomena, such as neural analogy-making and the linear representation hypothesis, suggest that supervised models can learn interpretable factors of variation in a linear fashion. Recent advances in self-supervised learning, particularly nonlinear Independent Component Analysis, have shown that these methods can recover latent structures by inverting the data generating process. We extend these identifiability results to parametric instance discrimination,
then show how insights transfer to the ubiquitous setting of supervised learning with cross-entropy minimization. We prove that even in standard classification tasks, models learn representations of ground-truth factors of variation up to a linear transformation under a certain DGP. We corroborate our theoretical contribution with a series of empirical studies. First, using simulated data matching our theoretical assumptions, we demonstrate successful disentanglement of latent factors. Second, we show that on DisLib, a widely-used disentanglement benchmark, simple classification tasks recover latent structures up to linear transformations. Finally, we reveal that models trained on ImageNet encode representations that permit linear decoding of proxy factors of variation.
Together, our theoretical findings and experiments offer a compelling explanation for recent observations of linear representations, such as superposition in neural networks. This work takes a significant step toward a cohesive theory that accounts for the unreasonable effectiveness of supervised learning. | supervised learning, representation learning, identifiability, linear representation hypothesis | We prove that models trained with cross-entropy in supervised learning can recover latent factors of the data-generating process up to a linear transformation, supported by both theoretical results and empirical evidence. | 10530 | 2410.21869 |
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues | https://openreview.net/forum?id=UvTo3tVBk2 | [
"Riccardo Grazzi",
"Julien Siems",
"Arber Zela",
"Jörg K.H. Franke",
"Frank Hutter",
"Massimiliano Pontil"
] | Oral | Linear Recurrent Neural Networks (LRNNs) such as Mamba, RWKV, GLA, mLSTM, and DeltaNet have emerged as efficient alternatives to Transformers for long sequences. However, both Transformers and LRNNs struggle to perform state-tracking, which may impair performance in tasks such as code evaluation. In one forward pass, current architectures are unable to solve even parity, the simplest state-tracking task, which non-linear RNNs can handle effectively. Recently, Sarrof et al. (2024) demonstrated that the failure of LRNNs like Mamba to solve parity stems from restricting the value range of their diagonal state-transition matrices to $[0, 1]$ and that incorporating negative values can resolve this issue. We extend this result to non-diagonal LRNNs such as DeltaNet. We prove that finite precision LRNNs with state-transition matrices having only positive eigenvalues cannot solve parity, while non-triangular matrices are needed to count modulo $3$. Notably, we also prove that LRNNs can learn any regular language when their state-transition matrices are products of identity minus vector outer product matrices, each with eigenvalues in the range $[-1, 1]$. Our experiments confirm that extending the eigenvalue range of Mamba and DeltaNet to include negative values not only enables them to solve parity but consistently improves their performance on state-tracking tasks. We also show that state-tracking enabled LRNNs can be pretrained stably and efficiently at scale (1.3B parameters), achieving competitive performance on language modeling and showing promise on code and math tasks. | State Tracking, State Space, Mamba, Linear RNN, Linear Attention, GLA, DeltaNet, Formal Languages, Products of Householders | We show that expanding the eigenvalue range of Linear RNN from [0, 1] to [-1,1] enhances their state-tracking capabilities, enabling them to solve complex tasks like parity and modular counting, while preserving their efficiency in language modeling. | 10504 | 2411.12537 |
Attention as a Hypernetwork | https://openreview.net/forum?id=V4K9h1qNxE | [
"Simon Schug",
"Seijin Kobayashi",
"Yassir Akram",
"Joao Sacramento",
"Razvan Pascanu"
] | Oral | Transformers can under some circumstances generalize to novel problem instances whose constituent parts might have been encountered during training, but whose compositions have not.
What mechanisms underlie this ability for compositional generalization?
By reformulating multi-head attention as a hypernetwork, we reveal that a composable, low-dimensional latent code specifies key-query specific operations.
We find empirically that this latent code is predictive of the subtasks the network performs on unseen task compositions, revealing that latent codes acquired during training are reused to solve unseen problem instances.
To further examine the hypothesis that the intrinsic hypernetwork of multi-head attention supports compositional generalization, we ablate whether making the hypernetwork-generated linear value network nonlinear strengthens compositionality.
We find that this modification improves compositional generalization on abstract reasoning tasks.
In particular, we introduce a symbolic version of the Raven's Progressive Matrices human intelligence test, which gives us precise control over the problem compositions encountered during training and evaluation.
We demonstrate on this task how scaling model size and data enables compositional generalization in transformers and gives rise to a functionally structured latent space. | attention, compositional generalization, abstract reasoning, in-context learning, transformer, mechanistic interpretability | Multiple heads in attention create reusable operations that support compositional generalization in abstract reasoning. | 10286 | 2406.05816 |
Transformers Provably Solve Parity Efficiently with Chain of Thought | https://openreview.net/forum?id=n2NidsYDop | [
"Juno Kim",
"Taiji Suzuki"
] | Oral | This work provides the first theoretical analysis of training transformers to solve complex problems by recursively generating intermediate states, analogous to fine-tuning for chain-of-thought (CoT) reasoning. We consider training a one-layer transformer to solve the fundamental $k$-parity problem, extending the work on RNNs by \citet{Wies23}. We establish three key results: (1) any finite-precision gradient-based algorithm, without intermediate supervision, requires substantial iterations to solve parity with finite samples. (2) In contrast, when intermediate parities are incorporated into the loss function, our model can learn parity in one gradient update when aided by \emph{teacher forcing}, where ground-truth labels of the reasoning chain are provided at each generation step. (3) Even without teacher forcing, where the model must generate CoT chains end-to-end, parity can be learned efficiently if augmented data is employed to internally verify the soundness of intermediate steps. Our findings, supported by numerical experiments, show that task decomposition and stepwise reasoning naturally arise from optimizing transformers with CoT; moreover, self-consistency checking can improve multi-step reasoning ability, aligning with empirical studies of CoT. | transformers, chain of thought, parity, self-consistency | null | 10210 | 2410.08633 |
Oscillatory State-Space Models | https://openreview.net/forum?id=GRMfXcAAFh | [
"T. Konstantin Rusch",
"Daniela Rus"
] | Oral | We propose Linear Oscillatory State-Space models (LinOSS) for efficiently learning on long sequences. Inspired by cortical dynamics of biological neural networks, we base our proposed LinOSS model on a system of forced harmonic oscillators. A stable discretization, integrated over time using fast associative parallel scans, yields the proposed state-space model. We prove that LinOSS produces stable dynamics only requiring nonnegative diagonal state matrix. This is in stark contrast to many previous state-space models relying heavily on restrictive parameterizations. Moreover, we rigorously show that LinOSS is universal, i.e., it can approximate any continuous and causal operator mapping between time-varying functions, to desired accuracy. In addition, we show that an implicit-explicit discretization of LinOSS perfectly conserves the symmetry of time reversibility of the underlying dynamics. Together, these properties enable efficient modeling of long-range interactions, while ensuring stable and accurate long-horizon forecasting. Finally, our empirical results, spanning a wide range of time-series tasks from mid-range to very long-range classification and regression, as well as long-horizon forecasting, demonstrate that our proposed LinOSS model consistently outperforms state-of-the-art sequence models. Notably, LinOSS outperforms Mamba and LRU by nearly 2x on a sequence modeling task with sequences of length 50k. | state-space models, sequence models, oscillators, long-range interactions, time-series | Oscillatory state-space models are provably able to learn long-range interactions of arbitrary length, are universal, and achieve state-of-the-art performance in practice. | 10203 | 2410.03943 |
Latent Bayesian Optimization via Autoregressive Normalizing Flows | https://openreview.net/forum?id=ZCOwwRAaEl | [
"Seunghun Lee",
"Jinyoung Park",
"Jaewon Chu",
"Minseo Yoon",
"Hyunwoo J. Kim"
] | Oral | Bayesian Optimization (BO) has been recognized for its effectiveness in optimizing expensive and complex objective functions.
Recent advancements in Latent Bayesian Optimization (LBO) have shown promise by integrating generative models such as variational autoencoders (VAEs) to manage the complexity of high-dimensional and structured data spaces.
However, existing LBO approaches often suffer from the value discrepancy problem, which arises from the reconstruction gap between input and latent spaces.
This value discrepancy problem propagates errors throughout the optimization process, leading to suboptimal outcomes.
To address this issue, we propose a Normalizing Flow-based Bayesian Optimization (NF-BO), which utilizes normalizing flow as a generative model to establish one-to-one encoding function from the input space to the latent space, along with its left-inverse decoding function, eliminating the reconstruction gap. Specifically, we introduce SeqFlow, an autoregressive normalizing flow for sequence data.
In addition, we develop a new candidate sampling strategy that dynamically adjusts the exploration probability for each token based on its importance.
Through extensive experiments, our NF-BO method demonstrates superior performance in molecule generation tasks, significantly outperforming both traditional and recent LBO approaches. | Bayesian optimization, normalizing flow | null | 10104 | null |
Energy-based Backdoor Defense Against Federated Graph Learning | https://openreview.net/forum?id=5Jc7r5aqHJ | [
"Guancheng Wan",
"Zitong Shi",
"Wenke Huang",
"Guibin Zhang",
"Dacheng Tao",
"Mang Ye"
] | Oral | Federated Graph Learning is rapidly evolving as a privacy-preserving collaborative approach. However, backdoor attacks are increasingly undermining federated systems by injecting carefully designed triggers that lead to the model making incorrect predictions. Trigger structures and injection locations in Federated Graph Learning are more diverse, making traditional federated defense methods less effective. In our work, we propose an effective Federated Graph Backdoor Defense using Topological Graph Energy (FedTGE). At the local client level, it injects distribution knowledge into the local model, assigning low energy to benign samples and high energy to the constructed malicious substitutes, and selects benign clients through clustering. At the global server level, the energy elements uploaded by each client are treated as new nodes to construct a global energy graph for energy propagation, making the selected clients' energy elements more similar and further adjusting the aggregation weights. Our method can handle high data heterogeneity, does not require a validation dataset, and is effective under both small and large malicious proportions. Extensive results on various settings of federated graph scenarios under backdoor attacks validate the effectiveness of this approach. | Federated Learning, Graph Learning | null | 10018 | null |
Reasoning Elicitation in Language Models via Counterfactual Feedback | https://openreview.net/forum?id=VVixJ9QavY | [
"Alihan Hüyük",
"Xinnuo Xu",
"Jacqueline R. M. A. Maasch",
"Aditya V. Nori",
"Javier Gonzalez"
] | Oral | Despite the increasing effectiveness of language models, their reasoning capabilities remain underdeveloped. In particular, causal reasoning through counterfactual question answering is lacking. This work aims to bridge this gap. We first derive novel metrics that balance accuracy in factual and counterfactual questions, capturing a more complete view of the reasoning abilities of language models than traditional factual-only based metrics. Second, we propose several fine-tuning approaches that aim to elicit better reasoning mechanisms, in the sense of the proposed metrics. Finally, we evaluate the performance of the fine-tuned language models in a variety of realistic scenarios. In particular, we investigate to what extent our fine-tuning approaches systemically achieve better generalization with respect to the base models in several problems that require, among others, inductive and deductive reasoning capabilities. | language models, reasoning, fine-tuning, counterfactuals | New approach to improve reasoning in language models via fine tuning with counterfactual synthetic data | 9840 | 2410.03767 |
CAX: Cellular Automata Accelerated in JAX | https://openreview.net/forum?id=o2Igqm95SJ | [
"Maxence Faldor",
"Antoine Cully"
] | Oral | Cellular automata have become a cornerstone for investigating emergence and self-organization across diverse scientific disciplines. However, the absence of a hardware-accelerated cellular automata library limits the exploration of new research directions, hinders collaboration, and impedes reproducibility. In this work, we introduce CAX (Cellular Automata Accelerated in JAX), a high-performance and flexible open-source library designed to accelerate cellular automata research. CAX delivers cutting-edge performance through hardware acceleration while maintaining flexibility through its modular architecture, intuitive API, and support for both discrete and continuous cellular automata in arbitrary dimensions. We demonstrate CAX's performance and flexibility through a wide range of benchmarks and applications. From classic models like elementary cellular automata and Conway's Game of Life to advanced applications such as growing neural cellular automata and self-classifying MNIST digits, CAX speeds up simulations up to 2,000 times faster. Furthermore, we demonstrate CAX's potential to accelerate research by presenting a collection of three novel cellular automata experiments, each implemented in just a few lines of code thanks to the library's modular architecture. Notably, we show that a simple one-dimensional cellular automaton can outperform GPT-4 on the 1D-ARC challenge. | cellular automata, emergence, self-organization, neural cellular automata | CAX is a high-performance and flexible open-source library designed to accelerate cellular automata research. | 9779 | 2410.02651 |
Proteina: Scaling Flow-based Protein Structure Generative Models | https://openreview.net/forum?id=TVQLu34bdw | [
"Tomas Geffner",
"Kieran Didi",
"Zuobai Zhang",
"Danny Reidenbach",
"Zhonglin Cao",
"Jason Yim",
"Mario Geiger",
"Christian Dallago",
"Emine Kucukbenli",
"Arash Vahdat",
"Karsten Kreis"
] | Oral | Recently, diffusion- and flow-based generative models of protein structures have emerged as a powerful tool for de novo protein design. Here, we develop *Proteina*, a new large-scale flow-based protein backbone generator that utilizes hierarchical fold class labels for conditioning and relies on a tailored scalable transformer architecture with up to $5\times$ as many parameters as previous models. To meaningfully quantify performance, we introduce a new set of metrics that directly measure the distributional similarity of generated proteins with reference sets, complementing existing metrics. We further explore scaling training data to millions of synthetic protein structures and explore improved training and sampling recipes adapted to protein backbone generation. This includes fine-tuning strategies like LoRA for protein backbones, new guidance methods like classifier-free guidance and autoguidance for protein backbones, and new adjusted training objectives. Proteina achieves state-of-the-art performance on de novo protein backbone design and produces diverse and designable proteins at unprecedented length, up to 800 residues. The hierarchical conditioning offers novel control, enabling high-level secondary-structure guidance as well as low-level fold-specific generation. | protein structure generation, de novo protein design, flow matching, fold class conditioning | We present a novel flow-based protein backbone generative model that uses a new scalable transformer architecture and conditions on fold class labels. | 9667 | 2503.00710 |
Residual Deep Gaussian Processes on Manifolds | https://openreview.net/forum?id=JWtrk7mprJ | [
"Kacper Wyrwal",
"Andreas Krause",
"Viacheslav Borovitskiy"
] | Oral | We propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks.
With manifold-to-manifold hidden layers and an arbitrary last layer, they can model manifold- and scalar-valued functions, as well as vector fields.
We target data inherently supported on manifolds, which is too complex for shallow Gaussian processes thereon.
For example, while the latter perform well on high-altitude wind data, they struggle with the more intricate, nonstationary patterns at low altitudes.
Our models significantly improve performance in these settings, enhancing prediction quality and uncertainty calibration, and remain robust to overfitting, reverting to shallow models when additional complexity is unneeded.
We further showcase our models on Bayesian optimisation problems on manifolds, using stylised examples motivated by robotics, and obtain substantial improvements in later stages of the optimisation process.
Finally, we show our models to have potential for speeding up inference for non-manifold data, when, and if, it can be mapped to a proxy manifold well enough. | Gaussian processes, manifolds, deep Gaussian processes, probabilistic methods, variational inference, uncertainty quantification, geometric learning | null | 9577 | 2411.00161 |
Learning to Discretize Denoising Diffusion ODEs | https://openreview.net/forum?id=xDrFWUmCne | [
"Vinh Tong",
"Dung Trung Hoang",
"Anji Liu",
"Guy Van den Broeck",
"Mathias Niepert"
] | Oral | Diffusion Probabilistic Models (DPMs) are generative models showing competitive performance in various domains, including image synthesis and 3D point cloud generation. Sampling from pre-trained DPMs involves multiple neural function evaluations (NFEs) to transform Gaussian noise samples into images, resulting in higher computational costs compared to single-step generative models such as GANs or VAEs. Therefore, reducing the number of NFEs while preserving generation quality is crucial. To address this, we propose LD3, a lightweight framework designed to learn the optimal time discretization for sampling. LD3 can be combined with various samplers and consistently improves generation quality without having to retrain resource-intensive neural networks. We demonstrate analytically and empirically that LD3 improves sampling efficiency with much less computational overhead. We evaluate our method with extensive experiments on 7 pre-trained models, covering unconditional and conditional sampling in both pixel-space and latent-space DPMs. We achieve FIDs of 2.38 (10 NFE), and 2.27 (10 NFE) on unconditional CIFAR10 and AFHQv2 in 5-10 minutes of training. LD3 offers an efficient approach to sampling from pre-trained diffusion models. Code is available at https://github.com/vinhsuhi/LD3. | Diffusion models, Efficient Sampling, Ordinary Differentiable Equations | null | 9534 | 2405.15506 |
Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment | https://openreview.net/forum?id=kGvXIlIVLM | [
"Huayu Chen",
"Hang Su",
"Peize Sun",
"Jun Zhu"
] | Oral | Classifier-Free Guidance (CFG) is a critical technique for enhancing the sample quality of visual generative models. However, in autoregressive (AR) multi-modal generation, CFG introduces design inconsistencies between language and visual content, contradicting the design philosophy of unifying different modalities for visual AR. Motivated by language model alignment methods, we propose Condition Contrastive Alignment (CCA) to facilitate guidance-free AR visual generation. Unlike guidance methods that alter the sampling process to achieve the ideal sampling distribution, CCA directly fine-tunes pretrained models to fit the same distribution target. Experimental results show that CCA can significantly enhance the guidance-free performance of all tested models with just one epoch of fine-tuning (1% of pretraining epochs) on the pretraining dataset. This largely removes the need for guided sampling in AR visual generation and cuts the sampling cost by half. Moreover, by adjusting training parameters, CCA can achieve trade-offs between sample diversity and fidelity similar to CFG. This experimentally confirms the strong theoretical connection between language-targeted alignment and visual-targeted guidance methods, unifying two previously independent research fields. | autoregressive, generative models, image generation, multimodal, alignment, RLHF, classifier-free guidance | null | 9473 | 2410.09347 |
TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis | https://openreview.net/forum?id=1CLzLXSFNn | [
"Shiyu Wang",
"Jiawei LI",
"Xiaoming Shi",
"Zhou Ye",
"Baichuan Mo",
"Wenze Lin",
"Ju Shengtong",
"Zhixuan Chu",
"Ming Jin"
] | Oral | Time series analysis plays a critical role in numerous applications, supporting tasks such as forecasting, classification, anomaly detection, and imputation. In this work, we present the time series pattern machine (TSPM), a model designed to excel in a broad range of time series tasks through powerful representation and pattern extraction capabilities. Traditional time series models often struggle to capture universal patterns, limiting their effectiveness across diverse tasks. To address this, we define multiple scales in the time domain and various resolutions in the frequency domain, employing various mixing strategies to extract intricate, task-adaptive time series patterns. Specifically, we introduce TimeMixer++, a general-purpose TSPM that processes multi-scale time series using (1) multi-resolution time imaging (MRTI), (2) time image decomposition (TID), (3) multi-scale mixing (MCM), and (4) multi-resolution mixing (MRM) to extract comprehensive temporal patterns. MRTI transforms multi-scale time series into multi-resolution time images, capturing patterns across both temporal and frequency domains. TID leverages dual-axis attention to extract seasonal and trend patterns, while MCM hierarchically aggregates these patterns across scales. MRM adaptively integrates all representations across resolutions. TimeMixer++ achieves state-of-the-art performance across 8 time series analytical tasks, consistently surpassing both general-purpose and task-specific models. Our work marks a promising step toward the next generation of TSPMs, paving the way for further advancements in time series analysis. | time series, pattern machine, predictive analysis | TimeMixer++ is a time series pattern machine that employs multi-scale and multi-resolution pattern extraction to deliver SOTA performance across 8 diverse analytical tasks, including forecasting, classification, anomaly detection, and imputation. | 9409 | 2410.16032 |
RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything | https://openreview.net/forum?id=1pXzC30ry5 | [
"Shilin Xu",
"Haobo Yuan",
"Qingyu Shi",
"Lu Qi",
"Jingbo Wang",
"Yibo Yang",
"Yining Li",
"Kai Chen",
"Yunhai Tong",
"Bernard Ghanem",
"Xiangtai Li",
"Ming-Hsuan Yang"
] | Oral | Recent segmentation methods, which adopt large-scale data training and transformer architecture, aim to create one foundation model that can perform multiple tasks.
However, most of these methods rely on heavy encoder and decoder frameworks, hindering their performance in real-time scenarios.
To explore real-time segmentation, recent advancements primarily focus on semantic segmentation within specific environments, such as autonomous driving. However, they often overlook the generalization ability of these models across diverse scenarios.
Therefore, to fill this gap, this work explores a novel real-time segmentation setting called real-time multi-purpose segmentation.
It contains three fundamental sub-tasks: interactive segmentation, panoptic segmentation, and video instance segmentation.
Unlike previous methods, which use a specific design for each task, we aim to use only a single end-to-end model to accomplish all these tasks in real-time.
To meet real-time requirements and balance multi-task learning, we present a novel dynamic convolution-based method, Real-Time Multi-Purpose SAM (RMP-SAM).
It contains an efficient encoder and an efficient decoupled adapter to perform prompt-driven decoding.
Moreover, we further explore different training strategies and one new adapter design to boost co-training performance further.
We benchmark several strong baselines by extending existing works to support our multi-purpose segmentation.
Extensive experiments demonstrate that RMP-SAM is effective and generalizes well on proposed benchmarks and other specific semantic tasks.
Our implementation of RMP-SAM achieves the optimal balance between accuracy and speed for these tasks. The code is released at
\url{https://github.com/xushilin1/RAP-SAM} | segment anything; real-time segmentation; multi-purpose model; | null | 9393 | null |
Steering Protein Family Design through Profile Bayesian Flow | https://openreview.net/forum?id=PSiijdQjNU | [
"Jingjing Gong",
"Yu Pei",
"Siyu Long",
"Yuxuan Song",
"Zhe Zhang",
"Wenhao Huang",
"Ziyao Cao",
"Shuyi Zhang",
"Hao Zhou",
"Wei-Ying Ma"
] | Oral | Protein family design emerges as a promising alternative by combining the advantages of de novo protein design and mutation-based directed evolution.In this paper, we propose ProfileBFN, the Profile Bayesian Flow Networks, for specifically generative modeling of protein families. ProfileBFN extends the discrete Bayesian Flow Network from an MSA profile perspective, which can be trained on single protein sequences by regarding it as a degenerate profile, thereby achieving efficient protein family design by avoiding large-scale MSA data construction and training. Empirical results show that ProfileBFN has a profound understanding of proteins. When generating diverse and novel family proteins, it can accurately capture the structural characteristics of the family. The enzyme produced by this method is more likely than the previous approach to have the corresponding function, offering better odds of generating diverse proteins with the desired functionality. | protein family generation, homologous protein generation, protein design, bayesian flow | null | 9363 | 2502.07671 |
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation | https://openreview.net/forum?id=ja4rpheN2n | [
"Ziwei Yang",
"Zheng Chen",
"Xin Liu",
"Rikuto Kotoge",
"Peng Chen",
"Yasuko Matsubara",
"Yasushi Sakurai",
"Jimeng Sun"
] | Oral | Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectively integrate gene interaction knowledge from databases or explicitly learn subtype-specific interactions. To address this mismatch, we propose GeSubNet, which learns a unified representation capable of predicting gene interactions while distinguishing between different disease subtypes. Graphs generated by such representations can be considered subtype-specific networks. GeSubNet is a multi-step representation learning framework with three modules: First, a deep generative model learns distinct disease subtypes from patient gene expression profiles. Second, a graph neural network captures representations of prior gene networks from knowledge databases, ensuring accurate physical gene interactions. Finally, we integrate these two representations using an inference loss that leverages graph generation capabilities, conditioned on the patient separation loss, to refine subtype-specific information in the learned representation. GeSubNet consistently outperforms traditional methods, with average improvements of 30.6%, 21.0%, 20.1%, and 56.6% across four graph evaluation metrics, averaged over four cancer datasets. Particularly, we conduct a biological simulation experiment to assess how the behavior of selected genes from over 11,000 candidates affects subtypes or patient distributions. The results show that the generated network has the potential to identify subtype-specific genes with an 83% likelihood of impacting patient distribution shifts. | Gene Functional Networks, Disease Subtypes, Bioinformatics | Gene interaction inference for disease subtype network generation | 9310 | 2410.13178 |
Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality | https://openreview.net/forum?id=4xWQS2z77v | [
"Sungyoon Kim",
"Aaron Mishkin",
"Mert Pilanci"
] | Oral | We discuss several aspects of the loss landscape of regularized neural networks: the structure of stationary points, connectivity of optimal solutions, path with non-increasing loss to arbitrary global optimum, and the nonuniqueness of optimal solutions, by casting the problem into an equivalent convex problem and considering its dual. Starting from two-layer neural networks with scalar output, we first characterize the solution set of the convex problem using its dual and further characterize all stationary points. With the characterization, we show that the topology of the global optima goes through a phase transition as the width of the network changes, and construct counterexamples where the problem may have a continuum of optimal solutions. Finally, we show that the solution set characterization and connectivity results can be extended to different architectures, including two layer vector-valued neural networks and parallel three-layer neural networks. | Convex duality, Machine Learning Theory, Loss Landscape, Optimal Sets | We investigate the loss landscape and topology of the optimal set of neural networks using convex duality. | 9266 | 2411.07729 |
Global Convergence in Neural ODEs: Impact of Activation Functions | https://openreview.net/forum?id=AoraWUmpLU | [
"Tianxiang Gao",
"Siyuan Sun",
"Hailiang Liu",
"Hongyang Gao"
] | Oral | Neural Ordinary Differential Equations (ODEs) have been successful in various applications due to their continuous nature and parameter-sharing efficiency. However, these unique characteristics also introduce challenges in training, particularly with respect to gradient computation accuracy and convergence analysis. In this paper, we address these challenges by investigating the impact of activation functions. We demonstrate that the properties of activation functions—specifically smoothness and nonlinearity—are critical to the training dynamics. Smooth activation functions guarantee globally unique solutions for both forward and backward ODEs, while sufficient nonlinearity is essential for maintaining the spectral properties of the Neural Tangent Kernel (NTK) during training. Together, these properties enable us to establish the global convergence of Neural ODEs under gradient descent in overparameterized regimes. Our theoretical findings are validated by numerical experiments, which not only support our analysis but also provide practical guidelines for scaling Neural ODEs, potentially leading to faster training and improved performance in real-world applications. | Neural ODEs, Gradient Descent, Neural Tangent Kernel (NTK) | null | 9256 | null |
MoDeGPT: Modular Decomposition for Large Language Model Compression | https://openreview.net/forum?id=8EfxjTCg2k | [
"Chi-Heng Lin",
"Shangqian Gao",
"James Seale Smith",
"Abhishek Patel",
"Shikhar Tuli",
"Yilin Shen",
"Hongxia Jin",
"Yen-Chang Hsu"
] | Oral | Large Language Models (LLMs) have significantly advanced AI with their exceptional performance across a wide range of tasks. However, their extensive computational requirements restrict their use on devices with limited resources.
While recent compression methods based on low-rank matrices show potential
solutions, they often suffer from significant loss of accuracy or introduce substantial
overhead in parameters and inference time. In this paper, we introduce Modular De-
composition (MoDeGPT), a new, efficient, and structured compression framework
that overcomes these limitations. MoDeGPT jointly decomposes pairs of consecu-
tive subcomponents within Transformer blocks, reduces hidden dimensions through
output reconstruction on a larger structural scale than conventional low-rank meth-
ods, and repurposes three classical matrix decomposition algorithms—Nyström
approximation, CR decomposition, and SVD—to ensure bounded errors in our
novel decomposition approach. Our experiments show that MoDeGPT, without
relying on backward propagation, consistently matches or surpasses the performance of prior techniques that depend on gradient information, while achieving a
98% reduction in compute costs when compressing a 13B-parameter model. On
LLaMA-2/3 and OPT models, MoDeGPT retains 90-95% of zero-shot performance
with compression rates of 25-30%. The compression process can be completed on
a single GPU in a few hours, boosting inference throughput by up to 46%. | LLM, model compression, matrix decomposition | A framework that expands matrix decomposition for LLM compression beyond SVD | 9191 | 2408.09632 |
MIND over Body: Adaptive Thinking using Dynamic Computation | https://openreview.net/forum?id=EjJGND0m1x | [
"Mrinal Mathur",
"Barak A. Pearlmutter",
"Sergey M. Plis"
] | Oral | While the human brain efficiently handles various computations with a limited number of neurons, traditional deep learning networks require a significant increase in parameters to improve performance.
Yet, these parameters are used inefficiently as the networks employ the same amount of computation for inputs of the same size, regardless of the input's complexity.
We address this inefficiency by introducing self-introspection capabilities to the network, enabling it to adjust the number of used parameters based on the internal representation of the task and adapt the computation time based on the task complexity.
This enables the network to adaptively reuse parameters across tasks, dynamically adjusting the computational effort to match the complexity of the input.
We demonstrate the effectiveness of this method on language modeling and computer vision tasks.
Notably, our model achieves 96.62\% accuracy on ImageNet with just a three-layer network, surpassing much larger ResNet-50 and EfficientNet. When applied to a transformer architecture, the approach achieves 95.8\%/88.7\% F1 scores on the SQuAD v1.1/v2.0 datasets at negligible parameter cost.
These results showcase the potential for dynamic and reflective computation, contributing to the creation of intelligent systems that efficiently manage resources based on input data complexity. | Interpretability, Fixed points, Dynamic routing, Dynamic input processing, Deep Learning Framework | We introduce MIND model that dynamically adjusts computation based on input complexity using an Introspection Network. It outperforms traditional architectures emulating the brain’s resource allocation for improved efficiency and performance. | 9112 | null |
From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions | https://openreview.net/forum?id=QKBu1BOAwd | [
"Changle Qu",
"Sunhao Dai",
"Xiaochi Wei",
"Hengyi Cai",
"Shuaiqiang Wang",
"Dawei Yin",
"Jun Xu",
"Ji-Rong Wen"
] | Oral | Tool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent in their pre-training data. In this process, tool documentation plays a crucial role by providing usage instructions for LLMs, thereby facilitating effective tool utilization. This paper concentrates on the critical challenge of bridging the comprehension gap between LLMs and external tools due to the inadequacies and inaccuracies inherent in existing human-centric tool documentation. We propose a novel framework, DRAFT, aimed at Dynamically Refining tool documentation through the Analysis of Feedback and Trials emanating from LLMs' interactions with external tools. This methodology pivots on an innovative trial-and-error approach, consisting of three distinct learning phases: experience gathering, learning from experience, and documentation rewriting, to iteratively enhance the tool documentation. This process is further optimized by implementing a diversity-promoting exploration strategy to ensure explorative diversity and a tool-adaptive termination mechanism to prevent overfitting while enhancing efficiency. Extensive experiments on multiple datasets demonstrate that DRAFT's iterative, feedback-based refinement significantly ameliorates documentation quality, fostering a deeper comprehension and more effective utilization of tools by LLMs. Notably, our analysis reveals that the tool documentation refined via our approach demonstrates robust cross-model generalization capabilities. | Large Language Model, Tool Learning, Learning from Experience | This paper proposes a novel framework aimed at dynamically adjusting and optimizing tool documentation based on the interaction feedback between LLMs and external tools. | 8990 | 2410.08197 |
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization | https://openreview.net/forum?id=VpWki1v2P8 | [
"Jui-Nan Yen",
"Si Si",
"Zhao Meng",
"Felix Yu",
"Sai Surya Duvvuri",
"Inderjit S Dhillon",
"Cho-Jui Hsieh",
"Sanjiv Kumar"
] | Oral | Low-rank adaption (LoRA) is a widely used parameter-efficient finetuning method for LLM that reduces memory requirements. However, current LoRA optimizers lack transformation invariance, meaning the updates depending on how the two LoRA factors are scaled or rotated. This deficiency leads to inefficient learning and sub-optimal solutions in practice. This paper introduces LoRA-RITE, a novel adaptive matrix preconditioning method for LoRA optimization, which can achieve transformation invariance and remain computationally efficient. We provide theoretical analysis to demonstrate the benefit of our method and conduct experiments on various LLM tasks with different models including Gemma 2B, 7B, and mT5-XXL. The results demonstrate consistent improvements against existing optimizers. For example, replacing Adam with LoRA-RITE during LoRA fine-tuning of Gemma-2B yielded 4.6% accuracy gain on Super-Natural Instructions and 3.5% accuracy gain across other four LLM benchmarks (HellaSwag, ArcChallenge, GSM8K, OpenBookQA). | optimization, LoRA | Improve the optimization of LoRA using adaptive matrix preconditioning method with transformation invariance | 8978 | 2410.20625 |
Scaling and evaluating sparse autoencoders | https://openreview.net/forum?id=tcsZt9ZNKD | [
"Leo Gao",
"Tom Dupre la Tour",
"Henk Tillman",
"Gabriel Goh",
"Rajan Troll",
"Alec Radford",
"Ilya Sutskever",
"Jan Leike",
"Jeffrey Wu"
] | Oral | Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing activations from a sparse bottleneck layer. Since language models learn many concepts, autoencoders need to be very large to recover all relevant features. However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents. We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. Additionally, we find modifications that result in few dead latents, even at the largest scales we tried. Using these techniques, we find clean scaling laws with respect to autoencoder size and sparsity. We also introduce several new metrics for evaluating feature quality based on the recovery of hypothesized features, the explainability of activation patterns, and the sparsity of downstream effects. These metrics all generally improve with autoencoder size. To demonstrate the scalability of our approach, we train a 16 million latent autoencoder on GPT-4 activations for 40 billion tokens. We release training code and autoencoders for open-source models, as well as a visualizer. | interpretability, sparse autoencoders, superposition, scaling laws | null | 8937 | 2406.04093 |
ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement | https://openreview.net/forum?id=YUYJsHOf3c | [
"XIANGYU PENG",
"Congying Xia",
"Xinyi Yang",
"Caiming Xiong",
"Chien-Sheng Wu",
"Chen Xing"
] | Oral | Post-training Large Language Models (LLMs) with explicit reasoning trajectories can enhance their reasoning abilities. However, acquiring such high-quality trajectory data typically demands meticulous supervision from humans or superior models, which can be either expensive or license-constrained. In this paper, we explore how far an LLM can improve its reasoning by self-synthesizing reasoning paths as training data without any additional supervision. Existing self-synthesizing methods, such as STaR, suffer from poor generalization to out-of-domain (OOD) reasoning tasks. We hypothesize it is due to that their self-synthesized reasoning paths are too task-specific, lacking general task-agnostic reasoning guidance. To address this, we propose **Reasoning Generalist via Self-Improvement (ReGenesis)**, a method to *self-synthesize reasoning paths as post-training data by progressing from abstract to concrete*. More specifically, ReGenesis self-synthesizes reasoning paths by converting general reasoning guidelines into task-specific ones, generating reasoning structures, and subsequently transforming these structures into reasoning paths, without the need for human-designed task-specific examples used in existing methods. We show that ReGenesis achieves superior performance on all in-domain and OOD settings tested compared to existing methods. For six OOD tasks specifically, while previous methods exhibited an average performance decrease of approximately 4.6% after post training, ReGenesis delivers around 6.1% performance improvement. We also conduct an in-depth analysis of our framework and show ReGenesis is effective across various language models and design choices. | LLM, reasoning, generalization, self-improvement | We propose ReGenesis, a method to self-synthesize reasoning paths as post-training data of LLMs by progressing from general reasoning structures to task-specific reasoning paths, to improve LLMs' generalization capability in reasoning. | 8842 | 2410.02108 |
Feedback Favors the Generalization of Neural ODEs | https://openreview.net/forum?id=cmfyMV45XO | [
"Jindou Jia",
"Zihan Yang",
"Meng Wang",
"Kexin Guo",
"Jianfei Yang",
"Xiang Yu",
"Lei Guo"
] | Oral | The well-known generalization problem hinders the application of artificial neural networks in continuous-time prediction tasks with varying latent dynamics. In sharp contrast, biological systems can neatly adapt to evolving environments benefiting from real-time feedback mechanisms. Inspired by the feedback philosophy, we present feedback neural networks, showing that a feedback loop can flexibly correct the learned latent dynamics of neural ordinary differential equations (neural ODEs), leading to a prominent generalization improvement. The feedback neural network is a novel two-DOF neural network, which possesses robust performance in unseen scenarios with no loss of accuracy performance on previous tasks. A linear feedback form is presented to correct the learned latent dynamics firstly, with a convergence guarantee. Then, domain randomization is utilized to learn a nonlinear neural feedback form. Finally, extensive tests including trajectory prediction of a real irregular object and model predictive control of a quadrotor with various uncertainties, are implemented, indicating significant improvements over state-of-the-art model-based and learning-based methods. | Neural ODEs, feedback, generalization, learning dynamical systems, model predictive control | null | 8713 | 2410.10253 |
Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs | https://openreview.net/forum?id=QWunLKbBGF | [
"Siyan Zhao",
"Mingyi Hong",
"Yang Liu",
"Devamanyu Hazarika",
"Kaixiang Lin"
] | Oral | Large Language Models (LLMs) are increasingly deployed as chatbots, yet their ability to personalize responses to user preferences remains limited. We introduce PrefEval, a benchmark for evaluating LLMs' ability to infer, memorize and adhere to user preferences in long-context conversational setting.
PrefEval comprises 3,000 manually curated user preference and query pairs spanning 20 topics. PrefEval contains user personalization or preference information in both explicit and implicit preference forms, and evaluates LLM performance using a generation and a classification task. With PrefEval, we have evaluated 10 open-sourced and
proprietary LLMs in multi-session conversations with varying context lengths up to 100k tokens. We benchmark with various prompting, iterative feedback, and retrieval-augmented generation methods.
Our benchmarking effort reveals that state-of-the-art LLMs face significant challenges in following users' preference during conversations. In particular, in zero-shot settings, preference following accuracy falls below 10\% at merely 10 turns (~3k tokens) across most evaluated models. Even with advanced prompting and retrieval methods, preference following still deteriorates in long-context conversations. Furthermore, we show that fine-tuning on PrefEval significantly improves performance. We believe PrefEval serves as a valuable resource for measuring, understanding, and enhancing LLMs' proactive preference following abilities, paving the way for personalized conversational agents. | personalization, benchmark, Large language models, conversational llm, chatbots | null | 8673 | 2502.09597 |
STAR: Synthesis of Tailored Architectures | https://openreview.net/forum?id=HsHxSN23rM | [
"Armin W Thomas",
"Rom Parnichkun",
"Alexander Amini",
"Stefano Massaroli",
"Michael Poli"
] | Oral | Iterative improvement of model architectures is fundamental to deep learning: Transformers first enabled scaling, and recent advances in model hybridization have pushed the quality-efficiency frontier. However, optimizing architectures remains challenging and expensive, with a variety of automated or manual approaches that fall short, due to limited progress in the design of search spaces and due to the simplicity of resulting patterns and heuristics. In this work, we propose a new approach for the synthesis of tailored architectures (STAR). Our approach combines a novel search space based on the theory of linear input-varying systems, supporting a hierarchical numerical encoding into architecture genomes. STAR genomes are automatically refined and recombined with gradient-free, evolutionary algorithms to optimize for multiple model quality and efficiency metrics. Using STAR, we optimize large populations of new architectures, leveraging diverse computational units and interconnection patterns, improving over highly-optimized Transformers and striped hybrid models on the frontier of quality, parameter size, and inference cache for autoregressive language modeling. | alternative architectures, deep signal processing, language models | We propose a new approach for automatic model architecture optimization (STAR), which combines a novel search space based on the theory of linear input-varying systems with a hierarchical numerical encoding into architecture genomes. | 8387 | 2411.17800 |
Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models | https://openreview.net/forum?id=WCRQFlji2q | [
"Javier Ferrando",
"Oscar Balcells Obeso",
"Senthooran Rajamanoharan",
"Neel Nanda"
] | Oral | Hallucinations in large language models are a widespread problem, yet the mechanisms behind whether models will hallucinate are poorly understood, limiting our ability to solve this problem. Using sparse autoencoders as an interpretability tool, we discover that a key part of these mechanisms is entity recognition, where the model detects if an entity is one it can recall facts about. Sparse autoencoders uncover meaningful directions in the representation space, these detect whether the model recognizes an entity, e.g. detecting it doesn't know about an athlete or a movie. This shows that models can have self-knowledge: internal representations about their own capabilities. These directions are causally relevant: capable of steering the model to refuse to answer questions about known entities, or to hallucinate attributes of unknown entities when it would otherwise refuse. We demonstrate that despite the sparse autoencoders being trained on the base model, these directions have a causal effect on the chat model's refusal behavior, suggesting that chat finetuning has repurposed this existing mechanism. Furthermore, we provide an initial exploration into the mechanistic role of these directions in the model, finding that they disrupt the attention of downstream heads that typically move entity attributes to the final token. | Mechanistic Interpretability, Hallucinations, Language Models | We use sparse autoencoders to identify directions that encode entity recognition in language models. | 8375 | 2411.14257 |
Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces | https://openreview.net/forum?id=AP0ndQloqR | [
"Saket Tiwari",
"Omer Gottesman",
"George Konidaris"
] | Oral | Advances in reinforcement learning (RL) have led to its successful application in complex tasks with continuous state and action spaces. Despite these advances in practice, most theoretical work pertains to finite state and action spaces. We propose building a theoretical understanding of continuous state and action spaces by employing a geometric lens to understand the locally attained set of states. The set of all parametrised policies learnt through a semi-gradient based approach induce a set of attainable states in RL. We show that training dynamics of a two layer neural policy induce a low dimensional manifold of attainable states embedded in the high-dimensional nominal state space trained using an actor-critic algorithm. We prove that, under certain conditions, the dimensionality of this manifold is of the order of the dimensionality of the action space. This is the first result of its kind, linking the geometry of the state space to the dimensionality of the action space. We empirically corroborate this upper bound for four MuJoCo environments and also demonstrate the results in a toy environment with varying dimensionality. We also show the applicability of this theoretical result by introducing a local manifold learning layer to the policy and value function networks to improve the performance in control environments with very high degrees of freedom by changing one layer of the neural network to learn sparse representations. | reinforcement learning, deep learning, geometry | We demonstrate, theoretically and empirically, that an RL agent with neural network policy induces a low-dimensional structure to the states sampled as its trajectories for deterministic environment | 8290 | null |
When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers | https://openreview.net/forum?id=vRvVVb0NAz | [
"Hongkang Li",
"Yihua Zhang",
"Shuai Zhang",
"Pin-Yu Chen",
"Sijia Liu",
"Meng Wang"
] | Oral | Task arithmetic refers to editing the pre-trained model by adding a weighted sum of task vectors, each of which is the weight update from the pre-trained model to fine-tuned models for certain tasks. This approach recently gained attention as a computationally efficient inference method for model editing, e.g., multi-task learning, forgetting, and out-of-domain generalization capabilities. However, the theoretical understanding of why task vectors can execute various conceptual operations remains limited, due to the highly non-convexity of training Transformer-based models. To the best of our knowledge, this paper provides the first theoretical characterization of the generalization guarantees of task vector methods on nonlinear Transformers. We consider a conceptual learning setting, where each task is a binary classification problem based on a discriminative pattern. We theoretically prove the effectiveness of task addition in simultaneously learning a set of irrelevant or aligned tasks, as well as the success of task negation in unlearning one task from irrelevant or contradictory tasks. Moreover, we prove the proper selection of linear coefficients for task arithmetic to achieve guaranteed generalization to out-of-domain tasks. All of our theoretical results hold for both dense-weight parameters and their low-rank approximations. Although established in a conceptual setting, our theoretical findings were validated on a practical machine unlearning task using the large language model Phi-1.5 (1.3B). | Task arithmetic, generalization, nonlinear Transformers, deep learning theory, machine unlearning | We provide the first theoretical characterization of the generalization guarantees of task vector methods on nonlinear Transformers. | 8179 | null |
Learning and aligning single-neuron invariance manifolds in visual cortex | https://openreview.net/forum?id=kbjJ9ZOakb | [
"Mohammad Bashiri",
"Luca Baroni",
"Ján Antolík",
"Fabian H. Sinz"
] | Oral | Understanding how sensory neurons exhibit selectivity to certain features and invariance to others is central to uncovering the computational principles underlying robustness and generalization in visual perception. Most existing methods for characterizing selectivity and invariance identify single or finite discrete sets of stimuli. Since these are only isolated measurements from an underlying continuous manifold, characterizing invariance properties accurately and comparing them across neurons with varying receptive field size, position, and orientation, becomes challenging. Consequently, a systematic analysis of invariance types at the population level remains under-explored. Building on recent advances in learning continuous invariance manifolds, we introduce a novel method to accurately identify and align invariance manifolds of visual sensory neurons, overcoming these challenges. Our approach first learns the continuous invariance manifold of stimuli that maximally excite a neuron modeled by a response-predicting deep neural network. It then learns an affine transformation on the pixel coordinates such that the same manifold activates another neuron as strongly as possible, effectively aligning their invariance manifolds spatially. This alignment provides a principled way to quantify and compare neuronal invariances irrespective of receptive field differences. Using simulated neurons, we demonstrate that our method accurately learns and aligns known invariance manifolds, robustly identifying functional clusters. When applied to macaque V1 neurons, it reveals functional clusters of neurons, including simple and complex cells. Overall, our method enables systematic, quantitative exploration of the neural invariance landscape, to gain new insights into the functional properties of visual sensory neurons. | neural invariances, invariance manifold, MEI, implicit neural representations, contrastive learning, invariance alignment, clustering, visual cortex, macaque V1, primary visual cortex | Our method learns single-neuron invariances and aligns them, enabling population-level exploration of neural invariances. | 8146 | null |
Feedback Schrödinger Bridge Matching | https://openreview.net/forum?id=k3tbMMW8rH | [
"Panagiotis Theodoropoulos",
"Nikolaos Komianos",
"Vincent Pacelli",
"Guan-Horng Liu",
"Evangelos Theodorou"
] | Oral | Recent advancements in diffusion bridges for distribution transport problems have heavily relied on matching frameworks, yet existing methods often face a trade-off between scalability and access to optimal pairings during training.
Fully unsupervised methods make minimal assumptions but incur high computational costs, limiting their practicality. On the other hand, imposing full supervision of the matching process with optimal pairings improves scalability, however, it can be infeasible in most applications.
To strike a balance between scalability and minimal supervision, we introduce Feedback Schrödinger Bridge Matching (FSBM), a novel semi-supervised matching framework that incorporates a small portion ($<8$% of the entire dataset) of pre-aligned pairs as state feedback to guide the transport map of non-coupled samples, thereby significantly improving efficiency. This is achieved by formulating a static Entropic Optimal Transport (EOT) problem with an additional term capturing the semi-supervised guidance. The generalized EOT objective is then recast into a dynamic formulation to leverage the scalability of matching frameworks. Extensive experiments demonstrate that FSBM accelerates training and enhances generalization by leveraging coupled pairs' guidance, opening new avenues for training matching frameworks with partially aligned datasets. | Diffusion models, Schrödinger bridge, Distribution matching, Semi-Supervised Learning | We introduce Feedback Schrödinger Bridge Matching, a novel semi-supervised framework that uses pre-aligned pairs to guide the matching of non-coupled samples. | 8128 | null |
TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes | https://openreview.net/forum?id=8enWnd6Gp3 | [
"Minghao Guo",
"Bohan Wang",
"Kaiming He",
"Wojciech Matusik"
] | Oral | We introduce TetSphere Splatting, a Lagrangian geometry representation designed for high-quality 3D shape modeling. TetSphere splatting leverages an underused yet powerful geometric primitive -- volumetric tetrahedral meshes. It represents 3D shapes by deforming a collection of tetrahedral spheres, with geometric regularizations and constraints that effectively resolve common mesh issues such as irregular triangles, non-manifoldness, and floating artifacts. Experimental results on multi-view and single-view reconstruction highlight TetSphere splatting's superior mesh quality while maintaining competitive reconstruction accuracy compared to state-of-the-art methods. Additionally, TetSphere splatting demonstrates versatility by seamlessly integrating into generative modeling tasks, such as image-to-3D and text-to-3D generation. | geometry representation, 3D modeling | null | 8079 | 2405.20283 |
Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents | https://openreview.net/forum?id=kxnoqaisCT | [
"Boyu Gou",
"Ruohan Wang",
"Boyuan Zheng",
"Yanan Xie",
"Cheng Chang",
"Yiheng Shu",
"Huan Sun",
"Yu Su"
] | Oral | Multimodal large language models (MLLMs) are transforming the capabilities of graphical user interface (GUI) agents, facilitating their transition from controlled simulations to complex, real-world applications across various platforms. However, the effectiveness of these agents hinges on the robustness of their grounding capability. Current GUI agents predominantly utilize text-based representations such as HTML or accessibility trees, which, despite their utility, often introduce noise, incompleteness, and increased computational overhead. In this paper, we advocate a human-like embodiment for GUI agents that perceive the environment entirely visually and directly perform pixel-level operations on the GUI. The key is visual grounding models that can accurately map diverse referring expressions of GUI elements to their coordinates on the GUI across different platforms. We show that a simple recipe, which includes web-based synthetic data and slight adaptation of the LLaVA architecture, is surprisingly effective for training such visual grounding models. We collect the largest dataset for GUI visual grounding so far, containing 10M GUI elements and their referring expressions over 1.3M screenshots, and use it to train UGround, a strong universal visual grounding model for GUI agents. Empirical results on six benchmarks spanning three categories (grounding, offline agent, and online agent) show that 1) UGround substantially outperforms existing visual grounding models for GUI agents, by up to 20\% absolute, and 2) agents with UGround outperform state-of-the-art agents, despite the fact that existing agents use additional text-based input while ours only uses visual perception. These results provide strong support for the feasibility and promises of GUI agents that navigate the digital world as humans do. | GUI Agents, Visual Grounding, Multimodal Large Language Models, GUI Grounding, Large Language Model | null | 8073 | 2410.05243 |
Progressive distillation induces an implicit curriculum | https://openreview.net/forum?id=wPMRwmytZe | [
"Abhishek Panigrahi",
"Bingbin Liu",
"Sadhika Malladi",
"Andrej Risteski",
"Surbhi Goel"
] | Oral | Knowledge distillation leverages a teacher model to improve the training of a student model. A persistent challenge is that a better teacher does not always yield a better student, to which a common mitigation is to use additional supervision from several “intermediate” teachers. One empirically validated variant of this principle is progressive distillation, where the student learns from successive intermediate checkpoints of the teacher. Using sparse parity as a sandbox, we identify an implicit curriculum as one mechanism through which progressive distillation accelerates the student’s learning. This curriculum is available only through the intermediate checkpoints but not the final converged one, and imparts both empirical acceleration and a provable sample complexity benefit to the student. We then extend our investigation to Transformers trained on probabilistic context-free grammars (PCFGs) and real-world pre-training datasets (Wikipedia and Books). Through probing the teacher model, we identify an analogous implicit curriculum where the model progressively learns features that capture longer context. Our theoretical and empirical findings on sparse parity, complemented by empirical observations on more complex tasks, highlight the benefit of progressive distillation via implicit curriculum across setups. | knowledge distillation, feature learning, curriculum, sparse parity, PCFG, optimization, MLP, Transformer | Progressive distillation accelerates the student model's training by providing an implicit curriculum through intermediate teacher checkpoints. | 8004 | 2410.05464 |
Rethinking Reward Modeling in Preference-based Large Language Model Alignment | https://openreview.net/forum?id=rfdblE10qm | [
"Hao Sun",
"Yunyi Shen",
"Jean-Francois Ton"
] | Oral | The Bradley-Terry (BT) model is a common and successful practice in reward modeling for Large Language Model (LLM) alignment. However, it remains unclear *why* this model --- originally developed for multi-player stochastic game matching --- can be adopted to convert pairwise response comparisons to reward values and make predictions. Especially given the fact that only a limited number of prompt-response pairs are sparsely compared with others.
In this paper, we first establish the convergence rate of BT reward models based on deep neural networks using embeddings, providing a theoretical foundation for their use.
Despite theoretically sound, we argue that the BT model is not a necessary choice from the perspective of downstream optimization, this is because a reward model only needs to preserve the correct ranking predictions through a monotonic transformation of the true reward.
We highlight the critical concept of *order consistency* in reward modeling and demonstrate that the BT model possesses this property.
Moreover, we propose a simple and straightforward upper-bound algorithm, compatible with off-the-shelf binary classifiers, as an alternative order-consistent reward modeling objective.
To offer practical insights, we empirically evaluate the performance of these different reward modeling approaches across more than 12,000 experimental setups, using $6$ base LLMs, $2$ datasets, and diverse annotation designs that vary in quantity, quality, and pairing choices in preference annotations. | Bradley-Terry Model, Reward Modeling, Large Language Models | null | 7959 | null |
Copyright-Protected Language Generation via Adaptive Model Fusion | https://openreview.net/forum?id=kRoWeLTpL4 | [
"Javier Abad",
"Konstantin Donhauser",
"Francesco Pinto",
"Fanny Yang"
] | Oral | The risk of language models reproducing copyrighted material from their training data has led to the development of various protective measures. Among these, inference-time strategies that impose constraints via post-processing have shown promise in addressing the complexities of copyright regulation. However, they often incur prohibitive computational costs or suffer from performance trade-offs. To overcome these limitations, we introduce Copyright-Protecting Model Fusion (CP-Fuse), a novel approach that combines models trained on disjoint sets of copyrighted material during inference. In particular, CP-Fuse adaptively aggregates the model outputs to minimize the reproduction of copyrighted content, adhering to a crucial balancing property to prevent the regurgitation of memorized data. Through extensive experiments, we show that CP-Fuse significantly reduces the reproduction of protected material without compromising the quality of text and code generation. Moreover, its post-hoc nature allows seamless integration with other protective measures, further enhancing copyright safeguards. Lastly, we show that CP-Fuse is robust against common techniques for extracting training data. | language models, copyright, model fusion, memorization, safety, privacy | null | 7825 | 2412.06619 |
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model | https://openreview.net/forum?id=ny8T8OuNHe | [
"Han Lin",
"Jaemin Cho",
"Abhay Zala",
"Mohit Bansal"
] | Oral | ControlNets are widely used for adding spatial control to text-to-image diffusion models. However, when it comes to controllable video generation, ControlNets cannot be directly integrated into new backbones due to feature space mismatches, and training ControlNets for new backbones can be a significant burden for many users. Furthermore, applying ControlNets independently to different frames can not effectively maintain object temporal consistency. To address these challenges, we introduce Ctrl-Adapter, an efficient and versatile framework that adds diverse controls to any image/video diffusion models through the adaptation of pretrained ControlNets. Ctrl-Adapter offers strong and diverse capabilities, including image and video control, sparse-frame video control, fine-grained patch-level multi-condition control, zero-shot adaptation to unseen conditions, and supports a variety of downstream tasks beyond spatial control, including video editing, video style transfer, and text-guided motion control. With six diverse U-Net/DiT-based image/video diffusion models (SDXL, PixArt-α, I2VGen-XL, SVD, Latte, Hotshot-XL), Ctrl-Adapter matches the performance of pretrained ControlNets on COCO and achieves the state-of-the-art on DAVIS 2017 with significantly lower computation (< 10 GPU hours). | Adapter, Diffusion, ControlNet, Text-to-video Generation, Image-to-video Generation, Text-to-image Generation | null | 7733 | null |
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models | https://openreview.net/forum?id=fAAaT826Vv | [
"Yu Feng",
"Ben Zhou",
"Weidong Lin",
"Dan Roth"
] | Oral | Predictive models often need to work with incomplete information in real-world tasks. Consequently, they must provide reliable probability or confidence estimation, especially in large-scale decision-making and planning tasks. Current large language models (LLMs) are insufficient for accurate estimations, but they can generate relevant factors that may affect the probabilities, produce coarse-grained probabilities when the information is more complete, and help determine which factors are relevant to specific downstream contexts. In this paper, we make use of these capabilities of LLMs to provide a significantly more accurate probabilistic estimation. We propose BIRD, a novel probabilistic inference framework that aligns a Bayesian network with LLM abductions and then estimates more accurate probabilities in a deduction step. We show BIRD provides reliable probability estimations that are 30% better than those provided directly by LLM baselines. These estimates further contribute to better and more trustworthy decision making. | Large language models, Reasoning, Planning, Trustworthiness, Interpretability, Probability Estimation, Bayesian Methods | null | 7711 | 2404.12494 |
LaMPlace: Learning to Optimize Cross-Stage Metrics in Macro Placement | https://openreview.net/forum?id=YLIsIzC74j | [
"Zijie Geng",
"Jie Wang",
"Ziyan Liu",
"Siyuan Xu",
"Zhentao Tang",
"Shixiong Kai",
"Mingxuan Yuan",
"Jianye HAO",
"Feng Wu"
] | Oral | Machine learning techniques have shown great potential in enhancing macro placement, a critical stage in modern chip design.
However, existing methods primarily focus on *online* optimization of *intermediate surrogate metrics* that are available at the current placement stage, rather than directly targeting the *cross-stage metrics*---such as the timing performance---that measure the final chip quality.
This is mainly because of the high computational costs associated with performing post-placement stages for evaluating such metrics, making the *online* optimization impractical.
Consequently, these optimizations struggle to align with actual performance improvements and can even lead to severe manufacturing issues.
To bridge this gap, we propose **LaMPlace**, which **L**earns **a** **M**ask for optimizing cross-stage metrics in macro placement.
Specifically, LaMPlace trains a predictor on *offline* data to estimate these *cross-stage metrics* and then leverages the predictor to quickly generate a mask, i.e., a pixel-level feature map that quantifies the impact of placing a macro in each chip grid location on the design metrics.
This mask essentially acts as a fast evaluator, enabling placement decisions based on *cross-stage metrics* rather than *intermediate surrogate metrics*.
Experiments on commonly used benchmarks demonstrate that LaMPlace significantly improves the chip quality across several key design metrics, achieving an average improvement of 9.6\%, notably 43.0\% and 30.4\% in terms of WNS and TNS, respectively, which are two crucial cross-stage metrics that reflect the final chip quality in terms of the timing performance. | Macro placement, Chip design, EDA | We propose a learning-based method for optimizing cross-stage metrics in macro placement. | 7707 | null |
miniCTX: Neural Theorem Proving with (Long-)Contexts | https://openreview.net/forum?id=KIgaAqEFHW | [
"Jiewen Hu",
"Thomas Zhu",
"Sean Welleck"
] | Oral | Real-world formal theorem proving often depends on a wealth of context, including definitions, lemmas, comments, file structure, and other information. We introduce $\texttt{miniCTX}$, which tests a model's ability to prove formal mathematical theorems that depend on new context that is not seen during training. $\texttt{miniCTX}$ contains theorems sourced from real Lean projects and textbooks, each associated with a context that can span tens of thousands of tokens. Models are tasked with proving a theorem given access to code from the theorem's repository, which contains context that is needed for the proof. As a baseline for $\texttt{miniCTX}$, we tested fine-tuning and prompting methods that condition theorem proving on preceding context. Both approaches substantially outperform traditional methods that rely solely on state information. We found that this ability to use context is not captured by previous benchmarks such as $\texttt{miniF2F}$. Alongside $\texttt{miniCTX}$, we offer $\texttt{ntp-toolkit}$ for automatically extracting and annotating theorem proving data, making it easy to add new projects into $\texttt{miniCTX}$ to ensure that contexts are not seen during training. $\texttt{miniCTX}$ offers a challenging and realistic evaluation of neural theorem provers. | Neural theorem proving, Formal mathematics, Benchmark dataset | We introduce a context-rich dataset and toolkit for evaluation of neural theorem proving under a more realistic scenario. | 7565 | null |
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions | https://openreview.net/forum?id=YrycTjllL0 | [
"Terry Yue Zhuo",
"Vu Minh Chien",
"Jenny Chim",
"Han Hu",
"Wenhao Yu",
"Ratnadira Widyasari",
"Imam Nur Bani Yusuf",
"Haolan Zhan",
"Junda He",
"Indraneil Paul",
"Simon Brunner",
"Chen GONG",
"James Hoang",
"Armel Randy Zebaze",
"Xiaoheng Hong",
"Wen-Ding Li",
"Jean Kaddour",
"Ming Xu",
"Zhihan Zhang",
"Prateek Yadav",
"et al. (13 additional authors not shown)"
] | Oral | Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks range from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing **diverse function calls as tools** to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately understanding **complex instructions**. Fulfilling both of these characteristics can pose a great challenge for LLMs. To assess how well LLMs can solve challenging and practical tasks via programs, we introduce BigCodeBench, a benchmark that challenges LLMs to invoke multiple function calls as tools from 139 libraries and 7 domains for 1,140 fine-grained tasks. To evaluate LLMs rigorously, each task encompasses 5.6 test cases with an average branch coverage of 99%. In addition, we propose a natural-language-oriented variant of BigCodeBench, BigCodeBench-Instruct, that automatically transforms the original docstrings into short instructions containing only essential information. Our extensive evaluation of 60 LLMs shows that **LLMs are not yet capable of following complex instructions to use function calls precisely, with scores up to 60%, significantly lower than the human performance of 97%**. The results underscore the need for further advancements in this area. | Code Generation, Tool Use, Instruction Following, Benchmark | null | 7553 | 2406.15877 |
Towards a Complete Logical Framework for GNN Expressiveness | https://openreview.net/forum?id=pqOjj90Vwp | [
"Tuo Xu"
] | Oral | Designing expressive Graph neural networks (GNNs) is an important topic in graph machine learning fields. Traditionally, the Weisfeiler-Lehman (WL) test has been the primary measure for evaluating GNN expressiveness. However, high-order WL tests can be obscure, making it challenging to discern the specific graph patterns captured by them. Given the connection between WL tests and first-order logic, some have explored the logical expressiveness of Message Passing Neural Networks. This paper aims to establish a comprehensive and systematic relationship between GNNs and logic. We propose a framework for identifying the equivalent logical formulas for arbitrary GNN architectures, which not only explains existing models, but also provides inspiration for future research. As case studies, we analyze multiple classes of prominent GNNs within this framework, unifying different subareas of the field. Additionally, we conduct a detailed examination of homomorphism expressivity from a logical perspective and present a general method for determining the homomorphism expressivity of arbitrary GNN models, as well as addressing several open problems. | graph neural networks, logic | Analyze the logical expressiveness of arbitrary graph neural networks | 7534 | null |
Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think | https://openreview.net/forum?id=DJSZGGZYVi | [
"Sihyun Yu",
"Sangkyung Kwak",
"Huiwon Jang",
"Jongheon Jeong",
"Jonathan Huang",
"Jinwoo Shin",
"Saining Xie"
] | Oral | Recent studies have shown that the denoising process in (generative) diffusion models can induce meaningful (discriminative) representations inside the model, though the quality of these representations still lags behind those learned through recent self-supervised learning methods. We argue that one main bottleneck in training large-scale diffusion models for generation lies in effectively learning these representations. Moreover, training can be made easier by incorporating high-quality external visual representations, rather than relying solely on the diffusion models to learn them independently. We study this by introducing a straightforward regularization called REPresentation Alignment (REPA), which aligns the projections of noisy input hidden states in denoising networks with clean image representations obtained from external, pretrained visual encoders. The results are striking: our simple strategy yields significant improvements in both training efficiency and generation quality when applied to popular diffusion and flow-based transformers, such as DiTs and SiTs. For instance, our method can speed up SiT training by over 17.5$\times$, matching the performance (without classifier-free guidance) of a SiT-XL model trained for 7M steps in less than 400K steps. In terms of final generation quality, our approach achieves state-of-the-art results of FID=1.42 using classifier-free guidance with the guidance interval. | Diffusion models, Representation learning | null | 7519 | 2410.06940 |
Classic but Everlasting: Traditional Gradient-Based Algorithms Converges Fast Even in Time-Varying Multi-Player Games | https://openreview.net/forum?id=t8FG4cJuL3 | [
"Yanzheng Chen",
"Jun Yu"
] | Oral | Last-iterate convergence behaviours of well-known algorithms are intensively investigated in various games, such as two-player bilinear zero-sum games.
However, most known last-iterate convergence properties rely on strict settings where the underlying games must have time-invariant payoffs.
Besides, the limited known attempts on the games with time-varying payoffs are in two-player bilinear time-varying zero-sum games and strictly monotone games. By contrast, in other time-varying games, the last-iterate behaviours of two classic algorithms, i.e., extra gradient (EG) and optimistic gradient (OG) algorithms, still lack research, especially the convergence rates in multi-player games.
In this paper, we investigate the last-iterate behaviours of EG and OG algorithms for convergent perturbed games, which extend upon the usual model of time-invariant games and incorporate external factors, such as vanishing noises.
Using the recently proposed notion of the tangent residual (or its modifications) as the potential function of games and the measure of proximity to the Nash equilibrium, we prove that the last-iterate convergence rates of EG and OG algorithms for perturbed games on bounded convex closed sets are $O({1}/{\sqrt{T}})$ if such games converge to monotone games at rates fast enough and that such a result holds true for certain unconstrained perturbed games. With this result, we address an open question
asking for the last-iterate convergence rate of EG and OG algorithms in constrained and time-varying settings. The above convergence rates are similar to known tight results on corresponding time-invariant games. | time-varying games, Nash equilibrium, extra gradient algorithm, optimistic gradient algorithm | null | 7489 | null |
DSPO: Direct Score Preference Optimization for Diffusion Model Alignment | https://openreview.net/forum?id=xyfb9HHvMe | [
"Huaisheng Zhu",
"Teng Xiao",
"Vasant G Honavar"
] | Oral | Diffusion-based Text-to-Image (T2I) models have achieved impressive success in generating high-quality images from textual prompts. While large language models (LLMs) effectively leverage Direct Preference Optimization (DPO) for fine-tuning on human preference data without the need for reward models, diffusion models have not been extensively explored in this area. Current preference learning methods applied to T2I diffusion models immediately adapt existing techniques from LLMs. However, this direct adaptation introduces an estimated loss specific to T2I diffusion models. This estimation can potentially lead to suboptimal performance through our empirical results. In this work, we propose Direct Score Preference Optimization (DSPO), a novel algorithm that aligns the pretraining and fine-tuning objectives of diffusion models by leveraging score matching, the same objective used during pretraining. It introduces a new perspective on preference learning for diffusion models. Specifically, DSPO distills the score function of human-preferred image distributions into pretrained diffusion models, fine-tuning the model to generate outputs that align with human preferences. We theoretically show that DSPO shares the same optimization direction as reinforcement learning algorithms in diffusion models under certain conditions. Our experimental results demonstrate that DSPO outperforms preference learning baselines for T2I diffusion models in human preference evaluation tasks and enhances both visual appeal and prompt alignment of generated images. | Text-to-image generation | null | 7413 | null |
TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation | https://openreview.net/forum?id=LbEWwJOufy | [
"Haiyang Liu",
"Xingchao Yang",
"Tomoya Akiyama",
"Yuantian Huang",
"Qiaoge Li",
"Shigeru Kuriyama",
"Takafumi Taketomi"
] | Oral | We present TANGO, a framework for generating co-speech body-gesture videos. Given a few-minute, single-speaker reference video and target speech audio, TANGO produces high-fidelity videos with synchronized body gestures. TANGO builds on Gesture Video Reenactment (GVR), which splits and retrieves video clips using a directed graph structure - representing video frames as nodes and valid transitions as edges. We address two key limitations of GVR: audio-motion misalignment and visual artifacts in GAN-generated transition frames. In particular, i) we propose retrieving gestures using latent feature distance to improve cross-modal alignment. To ensure the latent features could effectively model the relationship between speech audio and gesture motion, we implement a hierarchical joint embedding space (AuMoClip); ii) we introduce the diffusion-based model to generate high-quality transition frames. Our diffusion model, Appearance Consistent Interpolation (ACInterp), is built upon AnimateAnyone and includes a reference motion module and homography background flow to preserve appearance consistency between generated and reference videos. By integrating these components into the graph-based retrieval framework, TANGO reliably produces realistic, audio-synchronized videos and outperforms all existing generative and retrieval methods. Our code, pretrained models, and datasets are publicly available at https://github.com/CyberAgentAILab/TANGO. | co-speech video generation, cross-modal retrieval, audio repsentation learning, motion repsentation learning, video frame interpolation | null | 7366 | 2410.04221 |
Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation | https://openreview.net/forum?id=meRCKuUpmc | [
"Yang Tian",
"Sizhe Yang",
"Jia Zeng",
"Ping Wang",
"Dahua Lin",
"Hao Dong",
"Jiangmiao Pang"
] | Oral | Current efforts to learn scalable policies in robotic manipulation primarily fall into two categories: one focuses on "action," which involves behavior cloning from extensive collections of robotic data, while the other emphasizes "vision," enhancing model generalization by pre-training representations or generative models, also referred to as world models, using large-scale visual datasets. This paper presents an end-to-end paradigm that predicts actions using inverse dynamics models conditioned on the robot's forecasted visual states, named Predictive Inverse Dynamics Models (PIDM). By closing the loop between vision and action, the end-to-end PIDM can be a better scalable action learner. In practice, we use Transformers to process both visual states and actions, naming the model Seer. It is initially pre-trained on large-scale robotic datasets, such as DROID, and can be adapted to real-world scenarios with a little fine-tuning data. Thanks to large-scale, end-to-end training and the continuous synergy between vision and action at each execution step, Seer significantly outperforms state-of-the-art methods across both simulation and real-world experiments. It achieves improvements of 13% on the LIBERO-LONG benchmark, 22% on CALVIN ABC-D, and 43% in real-world tasks. Notably, it demonstrates superior generalization for novel objects, lighting conditions, and environments under high-intensity disturbances. Code and models will be publicly available. | Robotic Manipulation ; Pre-training ; Visual Foresight ; Inverse Dynamics ; Large-scale robot dataset | null | 7358 | 2412.15109 |
The Complexity of Two-Team Polymatrix Games with Independent Adversaries | https://openreview.net/forum?id=9VGTk2NYjF | [
"Alexandros Hollender",
"Gilbert Maystre",
"Sai Ganesh Nagarajan"
] | Oral | Adversarial multiplayer games are an important object of study in multiagent learning. In particular, polymatrix zero-sum games are a multiplayer setting where Nash equilibria are known to be efficiently computable. Towards understanding the limits of tractability in polymatrix games, we study the computation of Nash equilibria in such games where each pair of players plays either a zero-sum or a coordination game. We are particularly interested in the setting where players can be grouped into a small number of teams of identical interest. While the three-team version of the problem is known to be PPAD-complete, the complexity for two teams has remained open. Our main contribution is to prove that the two-team version remains hard, namely it is CLS-hard. Furthermore, we show that this lower bound is tight for the setting where one of the teams consists of multiple independent adversaries. On the way to obtaining our main result, we prove hardness of finding any stationary point in the simplest type of non-convex-concave min-max constrained optimization problem, namely for a class of bilinear polynomial objective functions. | algorithmic game theory, Nash equilibrium, minmax optimization | null | 7295 | 2409.07398 |
MMQA: Evaluating LLMs with Multi-Table Multi-Hop Complex Questions | https://openreview.net/forum?id=GGlpykXDCa | [
"Jian Wu",
"Linyi Yang",
"Dongyuan Li",
"Yuliang Ji",
"Manabu Okumura",
"Yue Zhang"
] | Oral | While large language models (LLMs) have made strides in understanding tabular data, current tabular evaluation benchmarks, such as WikiTableQuestions and WikiSQL, are focus on single-table scenarios, which cannot necessarily reflect the complexity of real-world applications. To bridge this gap, we present a \textbf{M}ulti-table and
Multi-hop Question Answering (MMQA) dataset to assess LLMs' understanding and reasoning capabilities in handling multi-table tasks. The MMQA dataset demands that models perform multiple inferences by drawing evidence from various tables, which are designed to be connected with each other and require models to identify and utilize relationships such as foreign and primary keys. Then, we introduce a comprehensive evaluation framework that tailors to assess LLMs' capabilities in several aspects including Multi-Table Retrieval, Text-to-SQL Generation, Multi-Table QA, Primary Key Selection, and Foreign Key Selection.
Finally, we propose a novel multi-table retrieval method that achieves state-of-the-art (SOTA) performance on the MMQA dataset compared to several strong baselines.
Our experiment results reveal that, compared with human performance, both open-source and commercial LLMs leave significant performance room for improvements in multi-table understanding and reasoning tasks. We believe that the MMQA benchmark will enhance and facilitate LLMs' multi-table capabilities in real-world scenarios. | LLM evaluation, multi-table question answering; multi-hop question answering | A novel multi-table evaluation benchmark that evaluate LLMs' multi-table understanding and reasoning ability. | 7281 | null |
On Scaling Up 3D Gaussian Splatting Training | https://openreview.net/forum?id=pQqeQpMkE7 | [
"Hexu Zhao",
"Haoyang Weng",
"Daohan Lu",
"Ang Li",
"Jinyang Li",
"Aurojit Panda",
"Saining Xie"
] | Oral | 3D Gaussian Splatting (3DGS) is increasingly popular for 3D reconstruction due to its superior visual quality and rendering speed. However, 3DGS training currently occurs on a single GPU, limiting its ability to handle high-resolution and large-scale 3D reconstruction tasks due to memory constraints. We introduce Grendel, a distributed system designed to partition 3DGS parameters and parallelize computation across multiple GPUs. As each Gaussian affects a small, dynamic subset of rendered pixels, Grendel employs sparse all-to-all communication to transfer the necessary Gaussians to pixel partitions and performs dynamic load balancing. Unlike existing 3DGS systems that train using one camera view image at a time, Grendel supports batched training with multiple views. We explore various optimization hyperparameter scaling strategies and find that a simple sqrt(batch-size) scaling rule is highly effective. Evaluations using large-scale, high-resolution scenes show that Grendel enhances rendering quality by scaling up 3DGS parameters across multiple GPUs. On the 4K ``Rubble'' dataset, we achieve a test PSNR of 27.28 by distributing 40.4 million Gaussians across 16 GPU, compared to a PSNR of 26.28 using 11.2 million Gaussians on a single GPU. Grendel is an open-source project available at: https://github.com/nyu-systems/Grendel-GS | Gaussian Splatting, Machine Learning System, Distributed Training | We describe a scalable distributed training system for 3D Guassian Splatting. | 7162 | 2406.18533 |
Emergence of meta-stable clustering in mean-field transformer models | https://openreview.net/forum?id=eBS3dQQ8GV | [
"Giuseppe Bruno",
"Federico Pasqualotto",
"Andrea Agazzi"
] | Oral | We model the evolution of tokens within a deep stack of Transformer layers as a continuous-time flow on the unit sphere, governed by a mean-field interacting particle system, building on the framework introduced in Geshkovski et al. (2023). Studying the corresponding mean-field Partial Differential Equation (PDE), which can be interpreted as a Wasserstein gradient flow, in this paper we provide a mathematical investigation of the long-term behavior of this system, with a particular focus on the emergence and persistence of meta-stable phases and clustering phenomena, key elements in applications like next-token prediction. More specifically, we perform a perturbative analysis of the mean-field PDE around the iid uniform initialization and prove that, in the limit of large number of tokens, the model remains close to a meta-stable manifold of solutions with a given structure (e.g., periodicity). Further, the structure characterizing the meta-stable manifold is explicitly identified, as a function of the inverse temperature parameter of the model, by the index maximizing a certain rescaling of Gegenbauer polynomials. | Mean-field limits, Transformers, Meta-stability, Clustering | null | 7124 | 2410.23228 |
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse | https://openreview.net/forum?id=1HCN4pjTb4 | [
"Arthur Jacot",
"Peter Súkeník",
"Zihan Wang",
"Marco Mondelli"
] | Oral | Deep neural networks (DNNs) at convergence consistently represent the training data in the last layer via a geometric structure referred to as neural collapse. This empirical evidence has spurred a line of theoretical research aimed at proving the emergence of neural collapse, mostly focusing on the unconstrained features model. Here, the features of the penultimate layer are free variables, which makes the model data-agnostic and puts into question its ability to capture DNN training. Our work addresses the issue, moving away from unconstrained features and studying DNNs that end with at least two linear layers. We first prove generic guarantees on neural collapse that assume \emph{(i)} low training error and balancedness of
linear layers (for within-class variability collapse), and \emph{(ii)} bounded conditioning of the features before the linear part (for orthogonality of class-means, and their alignment with weight matrices). The balancedness refers to the fact that $W_{\ell+1}^\top W_{\ell+1}\approx W_\ell W_\ell ^\top$ for any pair of
consecutive weight matrices
of the linear part, and the bounded conditioning requires a well-behaved ratio between largest and smallest non-zero singular values of the features. We then show that such assumptions hold for gradient descent training with weight decay: \emph{(i)} for networks with a wide first layer, we prove low training error and balancedness, and \emph{(ii)} for solutions that are either nearly optimal or stable under large learning rates, we additionally prove the bounded conditioning. Taken together, our results are the first to show neural collapse in the end-to-end training of DNNs. | neural collapse, gradient descent training, weight decay, balancedness | We consider deep neural networks with at least two final linear layers, and we show that neural collapse provably holds in the end-to-end training of the model with weight decay | 7009 | 2410.04887 |
ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials | https://openreview.net/forum?id=SBCMNc3Mq3 | [
"Pin Chen",
"Zexin Xu",
"Qing Mo",
"Hongjin Zhong",
"Fengyang Xu",
"Yutong Lu"
] | Oral | Supervised machine learning techniques are increasingly being adopted to speed up electronic structure predictions, serving as alternatives to first-principles methods like Density Functional Theory (DFT). Although current DFT datasets mainly emphasize chemical properties and atomic forces, the precise prediction of electronic charge density is essential for accurately determining a system's total energy and ground state properties. In this study, we introduce a novel electronic charge density dataset named ECD, which encompasses 140,646 stable crystal geometries with medium-precision Perdew–Burke–Ernzerhof (PBE) functional data. Within this dataset, a subset of 7,147 geometries includes high-precision electronic charge density data calculated using the Heyd–Scuseria–Ernzerhof (HSE) functional in DFT. By designing various benchmark tasks for crystalline materials and emphasizing training with large-scale PBE data while fine-tuning with a smaller subset of high-precision HSE data, we demonstrate the efficacy of current machine learning models in predicting electronic charge densities.
The ECD dataset and baseline models are open-sourced to support community efforts in developing new methodologies and accelerating materials design and applications. | Electronic Charge Density, Crystalline Inorganic Materials, Graph Neural Network, Dataset | A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials | 6903 | null |
On the Benefits of Memory for Modeling Time-Dependent PDEs | https://openreview.net/forum?id=o9kqa5K3tB | [
"Ricardo Buitrago",
"Tanya Marwah",
"Albert Gu",
"Andrej Risteski"
] | Oral | Data-driven techniques have emerged as a promising alternative to traditional numerical methods for solving PDEs. For time-dependent PDEs, many approaches are Markovian---the evolution of the trained system only depends on the current state, and not the past states. In this work, we investigate the benefits of using memory for modeling time-dependent PDEs: that is, when past states are explicitly used to predict the future. Motivated by the Mori-Zwanzig theory of model reduction, we theoretically exhibit examples of simple (even linear) PDEs, in which a solution that uses memory is arbitrarily better than a Markovian solution. Additionally, we introduce Memory Neural Operator (MemNO), a neural operator architecture that combines recent state space models (specifically, S4) and Fourier Neural Operators (FNOs) to effectively model memory. We empirically demonstrate that when the PDEs are supplied in low resolution or contain observation noise at train and test time, MemNO significantly outperforms the baselines without memory---with up to $6 \times$ reduction in test error. Furthermore, we show that this benefit is particularly pronounced when the PDE solutions have significant high-frequency Fourier modes (e.g., low-viscosity fluid dynamics) and we construct a challenging benchmark dataset consisting of such PDEs. | State Space Models, Partial Differential Equations | null | 6848 | 2409.02313 |
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