When Does Reasoning Matter? Unpacking the Contribution of Reasoning to LLM Performance
We're excited to share insights from our latest paper, "When Does Reasoning Matter? A Controlled Study of Reasoning’s Contribution to Model Performance", also made available on our Hugging Face Space.
Over the past few years, reasoning ability has become one of the central themes in debates about Large Language Models (LLMs). These models, which are adept at generating explicit Chains of Thought (CoT), consistently demonstrate state-of-the-art performance, especially in complex domains like math and coding. However, despite their empirical success, several crucial questions have remained underexplored: Which tasks truly benefit from reasoning — at what model scale, and at what cost compared to standard Instruction Fine-Tuning (IFT)?
Rigorously demonstrating these contributions and isolating confounding factors is often complicated by the expense and opacity of methods like Reinforcement Learning (RL), which are commonly used to refine reasoning strategies.
Our Controlled Approach: Synthetic Data
To address these questions, we developed a synthetic data distillation framework. This enabled us to conduct a supervised study, isolating reasoning signals without relying on computationally heavy or less transparent RL techniques, while still achieving comparable or superior performance gains (cf. Magistral Paper).
We started with an IFT dataset and used Qwen3-235B-A22B, a model with a configurable flag to enable or disable reasoning. This allowed us to create a pair of Reasoning and IFT-style answers for the same query. We applied this method to two query datasets: Infinity-Instruct and Llama-Nemotron-Post-Training, which resulted in the general-reasoning-ift-pairs and math-reasoning-ift-pairs datasets respectively. These two datasets are currently the largest collection of Reasoning-IFT pairs to date.
Using this setup, we analyzed the effect of reasoning on five models of varying sizes from the Qwen2.5 family: 0.5B, 1.5B, 3B, 7B, and 14B parameters. We then rigorously evaluated these models trained with our paired datasets across 12 diverse benchmarks, covering math-centric and general-purpose tasks in both multiple-choice and open-ended formats. This controlled environment enabled us to directly compare the performance of IFT and reasoning models across different scales and task types.
Key Takeaways from Our Study:
Our analysis revealed several critical insights into when and how reasoning capabilities contribute to LLM performance:
- Reasoning boosts performance.
Models trained with an explicit reasoning signal consistently demonstrated enhanced performance, frequently matching or exceeding larger IFT-only systems. Notably, these gains were most pronounced on math problems (e.g., gsm8k and aime) and open-ended tasks (e.g., ifeval, squad). Conversely, benefits on general multiple-choice tasks were more limited or inconsistent.
- The value of reasoning scales with model size and task complexity.
While standard IFT training remains Pareto-optimal in terms of inference efficiency up to the approximate 7B scale, the relationship shifts significantly beyond this threshold. At and above the 7B scale, reasoning models effectively overcome performance plateaus encountered by IFT models across all task types. This suggests that while scaling up model size with IFT yields Pareto-optimal gains in inference efficiency up to 7B parameters, the benefits of reasoning become more evident beyond this point.
- Characterization of tasks that benefit from reasoning
We characterized the benefits of reasoning across the different task types in our benchmark set. Our analysis involved plotting the accuracy delta (the performance gain) between a model trained on reasoning data and the same model trained on IFT data, as a function of the answer length delta. The answer length delta serves as a proxy for inference cost, since longer reasoning-based answers are inherently more expensive.
This plot confirmed a clear hierarchy of benefits:
Open-Ended Tasks (Highest Benefit): All open-ended tasks, extending beyond the math domain, showed the greatest benefit from reasoning.
Multiple-Choice Math Tasks (High Benefit): These tasks, which are inherently reasoning-intensive, followed the open-ended tasks in terms of accuracy gain.
General Multiple-Choice Questions (Modest Benefit): Consistent with prior observations, these tasks were the least receptive to reasoning. They yielded only modest performance gains despite producing significantly longer answers (higher inference cost).
In summary, reasoning data provides substantial benefits for complex and generative tasks but is less efficient for general multiple-choice questions, where the performance gain does not justify the increased inference cost.
If this preview of our results has piqued your interest, we encourage you to dive deeper into our paper for a comprehensive understanding of our methodology and findings, including the synergy between IFT and reasoning, a detailed training and inference cost analysis, and our bi-phasic training approach which simulates fine-tuning. We believe these insights offer valuable guidance for practitioners aiming to optimize LLM performance while carefully considering computational costs across diverse use cases.
Explore our generated datasets and trained models on our Hugging Face Space!
