ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL 事件

ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL arXiv:2606.01619v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strategies that generalize across tasks. Modular skills can provide such reusable strategies, yet existing skill-augmented RL methods decouple skill creation from policy optimization, risking adopting s