AI RESEARCH
ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
arXiv CS.AI
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ArXi:2606.01619v1 Announce Type: new 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 skills that conflict with the evolving policy. Inspired by Anthropic's Skill Creator, we.