Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement 事件

Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement arXiv:2605.26952v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has proven effective for training LLM-based agents with external tool-use capabilities. However, we identify that agentic RL training induces increasing redundant tool calls and blurs the model's intrinsic knowledge boundary, where the model fails to distinguish when tools are needed versus when parametric knowledge