← 研踪 PaperTrailarXiv:2606.04391 · v2
SkillAny: Online Skill Learning for Language Agents at Any Intermediate State
首次提交 2026-06-03 11:11
版本更新 2026-11-08 01:23
雷达观测 2026-11-10 11:14
摘要
Language agents increasingly rely on reusable skills to improve multi-step web automation across related tasks. A growing line of work studies online skill learning, where agents continually induce skills from previous task trajectories and reuse them in future tasks on the fly. However, existing methods mainly reuse skills at the task-level: a fixed set of skills is retrieved based on the initial task instruction and then held fixed throughout execution. This static strategy is misaligned with web execution, where the appropriate next action depends not only on the task goal but also on the current webpage state, which often transitions into situations that the initial skills fail to cover. To address this gap, we introduce SkillAny, an online skill learning framework that enables language agents to learn, retrieve, and invoke reusable skills at any intermediate state during task execution. Three components jointly enable this capability: sliding-window extraction turns completed trajectories into sub-procedures applicable beyond task boundaries, a dual text-code representation makes each skill both retrievable and executable, and step-level task-state retrieval selects skills that match the goal and the current webpage state as execution unfolds. Experiments on WebArena across five domains show that SkillAny consistently outperforms strong baselines, achieving average success rates of 37.5% with GPT-4.1 and 24.3% with Qwen3-4B, corresponding to relative gains of 10.6% and 10.0% over the strongest baseline, respectively. The code is available at https://github.com/plusnli/online-skill-learning.
作者备注：18 pages
观测到的版本
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v2 · 2026-11-08 01:23 · 最新观测版本
SkillAny: Online Skill Learning for Language Agents at Any Intermediate State
Language agents increasingly rely on reusable skills to improve multi-step web automation across related tasks. A growing line of work studies online skill learning, where agents continually induce skills from previous task trajectories and reuse them in future tasks on the fly. However, existing methods mainly reuse skills at the task-level: a fixed set of skills is retrieved based on the initial task instruction and then held fixed throughout execution. This static strategy is misaligned with web execution, where the appropriate next action depends not only on the task goal but also on the current webpage state, which often transitions into situations that the initial skills fail to cover. To address this gap, we introduce SkillAny, an online skill learning framework that enables language agents to learn, retrieve, and invoke reusable skills at any intermediate state during task execution. Three components jointly enable this capability: sliding-window extraction turns completed trajectories into sub-procedures applicable beyond task boundaries, a dual text-code representation makes each skill both retrievable and executable, and step-level task-state retrieval selects skills that match the goal and the current webpage state as execution unfolds. Experiments on WebArena across five domains show that SkillAny consistently outperforms strong baselines, achieving average success rates of 37.5% with GPT-4.1 and 24.3% with Qwen3-4B, corresponding to relative gains of 10.6% and 10.0% over the strongest baseline, respectively. The code is available at https://github.com/plusnli/online-skill-learning.
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