EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for
  Advantage Diversity
Authors: name: Xingjian Zhang, name: Siwei Wen, name: Wenjun Wu, name: Lei Huang

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.