One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling
The reasoning ability of large language models (LLMs) can be unleashed with reinforcement learning (RL) (OpenAI, 2024; DeepSeek-AI et al., 2025a; Zeng et al., 2025). The success of existing RL attempts in LLMs usually relies on high-quality samples of thousands or beyond. In this paper, we challenge fundamental assumptions about data requirements in RL for LLMs by demonstrating the remarkable effectiveness of one-shot learning. Specifically, we introduce polymath learning, a framework for designing one training sample that elicits multidisciplinary impact. We present three key findings: (1) A single, strategically selected math reasoning sample can produce significant performance improvements across multiple domains, including physics, chemistry, and biology with RL; (2) The math skills salient to reasoning suggest the characteristics of the optimal polymath sample; and (3) An engineered synthetic sample that integrates multidiscipline elements outperforms training with individual samples that naturally occur. Our approach achieves superior performance to training with larger datasets across various reasoning benchmarks, demonstrating that sample quality and design, rather than quantity, may be the key to unlock enhanced reasoning capabilities in language models. Our results suggest a shift, dubbed as sample engineering, toward precision engineering of training samples rather than simply increasing data volume.
6960f974138cc47cbd764d87
Yiyuan Li
6960f974138cc47cbd764d88
Zhen Huang
6960f974138cc47cbd764d89
Yanan Wu
6960f974138cc47cbd764d8a
Weixun Wang
6960f974138cc47cbd764d8b
Xuefeng Li
6960f974138cc47cbd764d8c
Yijia Luo
6960f974138cc47cbd764d8d
Wenbo Su
6960f974138cc47cbd764d8e
Bo Zheng
6960f974138cc47cbd764d8f
Pengfei Liu
claimed_verified
2026-01-19T09:25:57.053Z
6144a0c4ff1146bbd84d9865
https://cdn-avatars.huggingface.co/v1/production/uploads/1661715958139-6144a0c4ff1146bbd84d9865.png
Pengfei Liu
Pengfei
user
Pengfei
One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling
The reasoning ability of large language models (LLMs) can be unleashed with reinforcement learning (RL) (OpenAI, 2024; DeepSeek-AI et al., 2025a; Zeng et al., 2025). The success of existing RL attempts in LLMs usually relies on high-quality samples of thousands or beyond. In this paper, we challenge fundamental assumptions about data requirements in RL for LLMs by demonstrating the remarkable effectiveness of one-shot learning. Specifically, we introduce polymath learning, a framework for designing one training sample that elicits multidisciplinary impact. We present three key findings: (1) A single, strategically selected math reasoning sample can produce significant performance improvements across multiple domains, including physics, chemistry, and biology with RL; (2) The math skills salient to reasoning suggest the characteristics of the optimal polymath sample; and (3) An engineered synthetic sample that integrates multidiscipline elements outperforms training with individual samples that naturally occur. Our approach achieves superior performance to training with larger datasets across various reasoning benchmarks, demonstrating that sample quality and design, rather than quantity, may be the key to unlock enhanced reasoning capabilities in language models. Our results suggest a shift, dubbed as sample engineering, toward precision engineering of training samples rather than simply increasing data volume.
Reinforcement learning with carefully designed single training samples can significantly enhance the reasoning abilities of large language models across multiple disciplines, outperforming traditional approaches that rely on large datasets.
reinforcement learning large language models one-shot learning polymath learning mathematical reasoning sample engineering