SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
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 file's on-policy helpfulness, retaining only those from which the current policy still benefits within a linearly decaying budget, until the agent operates in a fully zero-shot setting. Extensive agentic experiments demonstrate that SKILL0 achieves substantial improvements over the standard RL baseline (+9.7\% for ALFWorld and +6.6\% for Search-QA), while maintaining a highly efficient context of fewer than 0.5k tokens per step. Our code is available at https://github.com/ZJU-REAL/SkillZero.
69cf1af5e02fdd68b91b7a12
Zhengxi Lu
claimed_verified
2026-04-03T09:07:58.508Z
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/avatars/dfd802a24bd63e509728159ebb1769f6.svg
Zhengxi Lu
LZXzju
user
LZXzju
69cf1af5e02fdd68b91b7a13
Zhiyuan Yao
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Jinyang Wu
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Chengcheng Han
69cf1af5e02fdd68b91b7a16
Qi Gu
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Xunliang Cai
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Weiming Lu
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Jun Xiao
69cf1af5e02fdd68b91b7a1a
Yueting Zhuang
69cf1af5e02fdd68b91b7a1b
Yongliang Shen
claimed_verified
2026-04-07T08:45:46.901Z
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/avatars/ce74839ba871f2b54313a670a233ba82.svg
Yongliang Shen
tricktreat
user
tricktreat
SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
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 file's on-policy helpfulness, retaining only those from which the current policy still benefits within a linearly decaying budget, until the agent operates in a fully zero-shot setting. Extensive agentic experiments demonstrate that SKILL0 achieves substantial improvements over the standard RL baseline (+9.7\% for ALFWorld and +6.6\% for Search-QA), while maintaining a highly efficient context of fewer than 0.5k tokens per step. Our code is available at https://github.com/ZJU-REAL/SkillZero.
SKILL0 enables LLM agents to internalize skills during training, allowing zero-shot autonomous behavior through a dynamic curriculum that reduces contextual overhead while improving task performance.
in-context reinforcement learning skill internalization dynamic curriculum tool invocation multi-turn task completion zero-shot setting ALFWorld Search-QA