LIMR: Less is More for RL Scaling

Releases

[2025/02/17] We're releasing the following components:

🛠️ LIM Tools: Implementation of our Learning Impact Measurement methodology
🚀 Training & Evaluation: Complete implementation of our training pipeline and evaluation scripts
🔥 LIMR Dataset: Our curated dataset of 1,389 mathematical questions
🤖 LIMR Model: Model training on the LIMR dataset.

Overview

This repository presents LIMR, an approach that challenges the assumption about data scaling in reinforcement learning for LLMs. We demonstrate that the quality and relevance of training samples matter far more than their quantity. Our Learning Impact Measurement (LIM) methodology enables automated evaluation of training sample effectiveness, eliminating the need for manual curation while achieving comparable or superior results with 6x less data. Notably, all our investigations are conducted directly from base models without distillation, providing clear insights into the core dynamics of RL training.

Our key findings revolutionize the understanding of RL training dynamics:

A strategically selected subset of training samples (1,389) can achieve comparable or even superior performance compared to training with the full dataset (8,523), fundamentally challenging the assumption that larger datasets necessarily lead to better performance.
We introduce Learning Impact Measurement (LIM), an automated quantitative method for probing the potential value of RL training samples, enabling systematic analysis of how different samples contribute to model improvement.
While distilled long-form reasoning data has shown efficiency in larger models, at the scale of ~1K samples with small models (7B), our data-efficient RL approach significantly outperforms SFT with distilled data.
The path to better reasoning capabilities may not lie in simply scaling up RL training data, but rather in being more selective about which samples to use.

Performance across challenging mathematical benchmarks:

Method	#Questions	AIME2024	MATH500	AMC2023	AVG.
Qwen-Math-7B	-	16.7	52.4	52.5	40.5
Qwen-Math-7B-FULL	8,523	32.5	76.6	61.9	57.0
Qwen-Math-7B-RAND	1,389	25.8	66.0	56.3	49.4
Qwen-Math-7B-LINEAR	1,138	28.3	74.6	61.9	54.9
LIMR	1,389	32.5	78.0	63.8	58.1

Comparsion with other popular RL recipes. We apply RL directly from the base model, without using distilled long chain-of-thought data from larger or stronger models, and only use 1k questions.

Methods	Init Model	Long CoT Dis.	#Questions
STILL-3	Instruct	Yes	29,925
DeepScaleR	Instruct	Yes	40,314
Sky-T1	Instruct	Yes	45,000
THUDM-T1	Instruct	No	30,000
PRIME	Instruct	No	150,000
SimpleRL	Base	No	8,523
LIMR	Base	No	1,389

Acknowledgements

Our work builds upon the insightful technical reports from DeepSeek R1 and Kimi-k1.5 teams. We extend our appreciation to the Qwen-Math team for their open-source model, and to the creators of OpenRLHF and vLLM for providing the essential reinforcement learning framework and inference infrastructure, respectively, that enabled this research.

Citation

If you find this work useful, please cite our paper:


@misc{limr2025,
  author = {Li, Xuefeng and Zou, Haoyang and Liu, Pengfei},
  title = {LIMR: Less is More for RL Scaling},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/GAIR-NLP/LIMR}},
}