Tom Lu

eigentom

https://eigentom.github.io

EigenTom

AI & ML interests

MLLM, Generative AI, Agentic RL

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upvoted a paper about 18 hours ago

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

upvoted a paper 1 day ago

Paper2Video: Automatic Video Generation from Scientific Papers

reacted to Kseniase's post with 👍 3 days ago

8 Emerging trends in Reinforcement Learning Reinforcement learning is having a moment - and not just this week. Some of its directions are already showing huge promise, while others are still early but exciting. Here’s a look at what’s happening right now in RL: 1. Reinforcement Pre-Training (RPT) → https://huggingface.co/papers/2506.08007 Reframes next-token pretraining as RL with verifiable rewards, yielding scalable reasoning gains 2. Reinforcement Learning from Human Feedback (RLHF) → https://huggingface.co/papers/1706.03741 The top approach. It trains a model using human preference feedback, building a reward model and then optimizing the policy to generate outputs people prefer 3. Reinforcement Learning with Verifiable Rewards (RLVR) → https://huggingface.co/papers/2506.14245 Moves from subjective (human-labeled) rewards to objective ones that can be automatically verified, like in math, code, or rubrics as reward, for example → https://huggingface.co/papers/2508.12790, https://huggingface.co/papers/2507.17746 4. Multi-objective RL → https://huggingface.co/papers/2508.07768 Trains LMs to balance multiple goals at once, like being helpful but also concise or creative, ensuring that improving one goal doesn’t ruin another 5. Parallel thinking RL → https://huggingface.co/papers/2509.07980 Trains parallel chains of thought, boosting math accuracy and final ceilings. It first teaches the model “parallel thinking” skill on easier problems, then uses RL to refine it on harder ones Read further below ⬇️ And if you like this, subscribe to the Turing post: https://www.turingpost.com/subscribe Also, check out our recent guide about the past, present and future of RL: https://www.turingpost.com/p/rlguide

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upvoted a paper about 18 hours ago

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

Paper • 2510.05592 • Published 2 days ago • 47

upvoted a paper 1 day ago

Paper2Video: Automatic Video Generation from Scientific Papers

Paper • 2510.05096 • Published 3 days ago • 75

reacted to Kseniase's post with 👍 3 days ago

Post

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8 Emerging trends in Reinforcement Learning

Reinforcement learning is having a moment - and not just this week. Some of its directions are already showing huge promise, while others are still early but exciting. Here’s a look at what’s happening right now in RL:

1. Reinforcement Pre-Training (RPT) → Reinforcement Pre-Training (2506.08007)
Reframes next-token pretraining as RL with verifiable rewards, yielding scalable reasoning gains

2. Reinforcement Learning from Human Feedback (RLHF) → Deep reinforcement learning from human preferences (1706.03741)
The top approach. It trains a model using human preference feedback, building a reward model and then optimizing the policy to generate outputs people prefer

3. Reinforcement Learning with Verifiable Rewards (RLVR) → Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs (2506.14245)
Moves from subjective (human-labeled) rewards to objective ones that can be automatically verified, like in math, code, or rubrics as reward, for example → Reinforcement Learning with Rubric Anchors (2508.12790), Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains (2507.17746)

4. Multi-objective RL → Pareto Multi-Objective Alignment for Language Models (2508.07768)
Trains LMs to balance multiple goals at once, like being helpful but also concise or creative, ensuring that improving one goal doesn’t ruin another

5. Parallel thinking RL → Parallel-R1: Towards Parallel Thinking via Reinforcement Learning (2509.07980)
Trains parallel chains of thought, boosting math accuracy and final ceilings. It first teaches the model “parallel thinking” skill on easier problems, then uses RL to refine it on harder ones

Read further below ⬇️
And if you like this, subscribe to the Turing post: https://www.turingpost.com/subscribe

Also, check out our recent guide about the past, present and future of RL: https://www.turingpost.com/p/rlguide