SkillRise Collapses Extract-Retrieve-Execute Into One RL Loop: a Single Policy Alternates Between Solving Tasks and Rewriting Its Own Skill Document

arXiv / HuggingFace Daily Papers·medium signalXBlueskyLinkedInCopy link

Submitted July 29 by Zhiyuan Yao and 15 co-authors, SkillRise orders task instances into progressively harder sequences and trains one policy to alternate between solving a task and refining a skill document that carries forward to the next one. Gains over the strongest baseline run 2.3 to 8.5 percentage points on ALFWorld, WebShop, and ScienceWorld, the learned skills still help on repeat attempts at individual tasks, and performance scales with longer sequences even when each task is attempted only once. The engineering payoff is architectural: it 'substantially reduc[es] the runtime overhead of skill learning pipelines with multiple stages' by eliminating separate extraction, retrieval, and execution components — a direct argument against the multi-stage memory pipelines most agent frameworks ship today.