Meta-Skill Learning for Tool-Using Environment Adaptation in Large Language Model Agents

Large language model (LLM) agents can often generalize to new tool-using environments from pre-prepared tool definitions and environment descriptions; however, such specifications rarely capture the implicit operational rules that govern success in non-standard environments. Interaction trajectories instead provide rich environment-specific knowledge, exposing structured patterns of success, failure, and recovery, and suggesting that such experience can be systematically distilled into reusable skills for accomplishing new tasks within the same environment. In this paper, we study training an LLM agent to rapidly adapt to new environments by learning a meta-skill, defined as the ability to explore a new environment, summarize environment-specific operational knowledge from limited interactions, and apply the resulting skill to subsequent tasks. We formulate this problem as meta-reinforcement learning over a distribution of tool-using environments and propose a meta-skill learning framework for environment adaptation. Experiments on multiple benchmarks show that environment-specific adaptation consistently improves performance on new tasks, supporting that tasks within the same environment share reusable skills. Moreover, the proposed framework outperforms strong baselines on both in-distribution and out-of-distribution environments.