AI RESEARCH
CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution
arXiv CS.CL
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ArXi:2604.15840v1 Announce Type: new Reinforcement learning for LLM agents is typically conducted on a static data distribution, which fails to adapt to the agent's evolving behavior and leads to poor coverage of complex environment interactions. To address these challenges, we propose CoEvolve, an agent-data mutual evolution framework that enables LLM agents to improve through closed-loop, interaction-driven