EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for Advantage Diversity

Zhang Xingjian, Siwei Wen, Wenjun Wu, Lei Huang

EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for Advantage Diversity: 9 upvotes on Hugging Face Daily Papers, #17 of 22 papers on 2025-07-29. Day-by-day upvote history.

Large Language Models (LLMs) have made remarkable progress in enhancing step-by-step reasoning through reinforcement learning. However, the Group Relative Policy Optimization (GRPO) algorithm, which relies on sparse reward rules, often encounters the issue of identical rewards within groups, leading to the advantage collapse problem. Existing works typically address this challenge from two perspectives: enforcing model reflection to enhance response diversity, and introducing internal feedback to augment the training signal (advantage). In this work, we begin by analyzing the limitations of model reflection and investigating the policy entropy of responses at the fine-grained sample level. Based on our experimental findings, we propose the EDGE-GRPO algorithm, which adopts Entropy-Driven Advantage and Guided Error Correction to effectively mitigate the problem of advantage collapse. Extensive experiments on several main reasoning benchmarks demonstrate the effectiveness and superiority of our approach. It is available at https://github.com/ZhangXJ199/EDGE-GRPO.

Paper page on Hugging Face · arXiv

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