Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning 事件

Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning arXiv:2606.03762v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks. However, integrating external tools often destabilizes training: over-reliance on tools can induce input distribution shift, while overly conservative tool use limits effective exploration. To address thi