LLM Agent Skill Learning

This field asks how language-model agents can turn experience, demonstrations, external guides, or feedback into reusable procedures for tasks such as tool use, web navigation, coding, and embodied control. Common approaches distill skills from successful and failed trajectories into text, executable programs, or hierarchical libraries, then retrieve, compose, verify, and revise them; other methods train agents to internalize skills through supervised fine-tuning or reinforcement learning. Work is evaluated on interactive benchmarks such as ALFWorld, WebShop, AppWorld, and coding tasks, with attention to success, transfer, token cost, and consistency. A central challenge is that skills can help when relevant but cause regressions when stale, poorly selected, or applied outside their scope, making reliable validation, lifecycle management, and safety essential.