StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning 事件

StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning arXiv:2604.18401v2 Announce Type: replace Abstract: Agentic reinforcement learning (RL) is emerging as a critical post-training paradigm for improving LLM agent capabilities. Existing RL algorithms for LLMs largely follow the token-centric paradigm as in RLHF and RLVR, where tokens serve as the basic units for modeling and optimization. However, this paradigm introduces a granularity mismatch in agentic RL, as it optimi