SEAL: Synergistic Co-Evolution of Agents and Learning Environments 事件

SEAL: Synergistic Co-Evolution of Agents and Learning Environments arXiv:2605.24426v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly improved through interaction, yet most self-evolution methods adapt either the policy or the learning environment in isolation. We identify this structural gap as \emph{Agent-Environment Misalignment}: the agent's capability frontier changes during training, while the environment that provides supervision remains static or only wea