AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning

A research article presents AEM (Adaptive Entropy Modulation), a method for credit assignment in reinforcement learning (RL) that does not require supervision, specifically for large language model (LLM) agents. This technique tackles the issue of sparse rewards that only reflect outcomes in multi-turn tasks by adaptively adjusting entropy dynamics during RL training, thereby enhancing the balance between exploration and exploitation. AEM theoretically advances entropy analysis from the token level to the response level, minimizing token sampling variance and effectively managing entropy drift with natural gradients. The method seeks to eliminate the reliance on dense intermediate supervision, such as reward models or auxiliary self-supervised signals, which often fail to generalize well across various tasks and domains. The paper is available on arXiv with the identifier 2605.00425.