[2604.02268] SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

Computer Science > Machine Learning arXiv:2604.02268 (cs) [Submitted on 2 Apr 2026] Title:SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization Authors:Zhengxi Lu, Zhiyuan Yao, Jinyang Wu, Chengcheng Han, Qi Gu, Xunliang Cai, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen View a PDF of the paper titled SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization, by Zhengxi Lu and 9 other authors View PDF HTML (experimental) Abstract:Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes substantial token overhead, and the model never truly acquires the knowledge it merely follows. We ask whether skills can instead be internalized into model parameters, enabling zero-shot autonomous behavior without any runtime skill retrieval. We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization. SKILL0 introduces a training-time curriculum that begins with full skill context and progressively withdraws it. Skills are grouped offline by category and rendered with interaction history into a compact visual context, teaching he model tool invocation and multi-turn task completion. A Dynamic Curriculum then evaluates each skill...