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 weakly coupled to the agent's revealed failures. We propose SEAL, a closed-loop co-evolution framework for interactive tool-use agents. SEAL collects on-policy trajectorie
The paper identifies a crucial structural issue in current agent training, 'Agent-Environment Misalignment,' highlighting the need for more dynamic and integrated evolution methods as LLM agents mature.
This research addresses a core limitation in AI agent development, potentially accelerating their autonomy and effectiveness across various applications by improving how they learn and adapt.
The proposed SEAL framework introduces a closed-loop co-evolution of agents and their learning environments, moving beyond isolated policy or environment adaptation.
- · AI agent developers
- · Companies deploying autonomous AI
- · AI research institutions
- · Tool-use agent platforms
- · Static AI training methodologies
- · Manual environment design
- · Limited-scope agent training platforms
More robust, adaptive, and capable AI agents will emerge as this co-evolution approach gains traction.
The improved performance of agents could lead to faster automation of complex tasks in white-collar sectors.
The development of highly autonomous tool-using agents may accelerate the broader adoption of AI agents, potentially impacting labor markets and enterprise software ecosystems.
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