RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner
cs.AI, cs.LG
Submitted: 2024-10-31
Updated: 2026-09-20
Journal ref: ICLR 2025 Workshop on Reasoning and Planning for Large Language Models
Code: https://github.com/d09942015ntu/rl_star
License: http://creativecommons.org/licenses/by/4.0/
The gist: The reasoning abilities of large language models (LLMs) have improved with chain-of-thought (CoT) prompting, allowing models to solve complex tasks stepwise.
Terminology
Abstract
The reasoning abilities of large language models (LLMs) have improved with chain-of-thought (CoT) prompting, allowing models to solve complex tasks stepwise. However, training CoT capabilities requires detailed reasoning data, which is often scarce. The self-taught reasoner (STaR) framework addresses this by using reinforcement learning to automatically generate reasoning steps, reducing reliance on human-labeled data. Although STaR and its variants have demonstrated empirical success, a theoretical foundation explaining these improvements is lacking. This work provides a theoretical framework for understanding the effectiveness of reinforcement learning on CoT reasoning and STaR. Our contributions are: (1) criteria for the quality of pre-trained models necessary to initiate effective reasoning improvement; (2) an analysis of policy improvement, showing why LLM reasoning improves iteratively with STaR; (3) conditions for convergence to an optimal reasoning policy; and (4) an examination of STaR's robustness, explaining how it can improve reasoning even when incorporating occasional incorrect steps. We also run RL-STaR on GPT-2, Qwen2.5-0.5B and Phi-3-mini, and the measured return curves follow the ones the analysis predicts. This framework bridges empirical findings with theoretical insights, advancing reinforcement learning approaches for reasoning in LLMs.
Sources
- How Likely Do LLMs with CoT Mimic Human Reasoning?
- Transformers Provably Solve Parity Efficiently with Chain of Thought
- Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning
- Lean-STaR: Learning to Interleave Thinking and Proving
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
- A Theory for Length Generalization in Learning to Reason
- Automatic Chain of Thought Prompting in Large Language Models
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