From History to State: Constant-Context Skill Learning for LLM Agents

This paper proposes constant-context skill learning, a novel framework for LLM agents to manage recurring workflows more efficiently. It addresses privacy, cost, and capability challenges by learning reusable procedures in task-family modules and conditioning inference on a compact state block. Its effectiveness is demonstrated across benchmarks like ALFWorld, WebShop, and SciWorld.