<ul><li>A new algorithm named Retrospective Replay-based Reinforcement Learning (RRL) has been proposed to improve RL exploration for large language models (LLMs).</li><li>During the early stages of training, LLMs exhibit strong exploratory capabilities, but are limited in their ability to solve complex problems.</li><li>RRL introduces a dynamic replay mechanism throughout the training process, allowing the model to revisit and re-explore promising states identified in the early stages.</li><li>Experimental results show that RRL significantly enhances the effectiveness of RL in optimizing LLMs for complicated reasoning tasks.</li></ul>

Improving RL Exploration for LLM Reasoning through Retrospective Replay

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