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

Reinforcement learning (RL) has substantially improved the ability of large language model (LLM) agents to interact with environments and solve multi-turn tasks. However, effective agentic RL remains challenging: sparse outcome-only rewards provide limited guidance for assigning credit to individual steps within long interaction trajectories. Existing approaches often introduce dense intermediate supervision, such as process reward models or auxiliary self-supervised signals, which increases sup

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ethics.ai (1 May 2026), “AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning,” evidence record 5127, https://ethics.ai/record/5127 (originally published by arXiv).

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