<ul><li>The theoretical analysis of reinforcement learning frameworks for self-taught reasoner (STaR) in large language models (LLMs) is presented.</li><li>STaR framework uses reinforcement learning to generate reasoning steps and reduce the dependence on human-labeled data.</li><li>The analysis provides a theoretical understanding of the effectiveness of reinforcement learning on chain-of-thought (CoT) reasoning and STaR.</li><li>The framework explores criteria for pre-trained models, policy improvement, convergence, and the robustness of STaR in improving reasoning in LLMs.</li></ul>

RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner

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