title: SkillWeaver: Web Agents can Self-Improve by Discovering and Honing
    Skills
summary: To survive and thrive in complex environments, humans have evolved
  sophisticated self-improvement mechanisms through environment exploration,
  hierarchical abstraction of experiences into reuseable skills, and
  collaborative construction of an ever-growing skill repertoire. Despite recent
  advancements, autonomous web agents still lack crucial self-improvement
  capabilities, struggling with procedural knowledge abstraction, refining
  skills, and skill composition. In this work, we introduce SkillWeaver, a
  skill-centric framework enabling agents to self-improve by autonomously
  synthesizing reusable skills as APIs. Given a new website, the agent
  autonomously discovers skills, executes them for practice, and distills
  practice experiences into robust APIs. Iterative exploration continually
  expands a library of lightweight, plug-and-play APIs, significantly enhancing
  the agent's capabilities. Experiments on WebArena and real-world websites
  demonstrate the efficacy of SkillWeaver, achieving relative success rate
  improvements of 31.8% and 39.8%, respectively. Additionally, APIs synthesized
  by strong agents substantially enhance weaker agents through transferable
  skills, yielding improvements of up to 54.3% on WebArena. These results
  demonstrate the effectiveness of honing diverse website interactions into APIs,
  which can be seamlessly shared among various web agents.
paper_authors._id: 67f71c8ff13d9b7b2194b772
paper_authors.name: Boyuan Zheng
paper_authors._id: 67f71c8ff13d9b7b2194b773
paper_authors.name: Michael Y. Fatemi
paper_authors._id: 67f71c8ff13d9b7b2194b774
paper_authors.name: Xiaolong Jin
paper_authors._id: 67f71c8ff13d9b7b2194b775
paper_authors.name: Zora Zhiruo Wang
paper_authors._id: 67f71c8ff13d9b7b2194b776
paper_authors.name: Apurva Gandhi
paper_authors._id: 67f71c8ff13d9b7b2194b777
paper_authors.name: Yueqi Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-17T08:05:29.717Z
paper_authors._id: 63e7bf7be02ee67e8e53f78d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63e7bf7be02ee67e8e53f78d/z9j1PWrurEyIA2JXzpz0i.png
paper_authors.fullname: Yueqi Song
paper_authors.type: user
paper_authors.user: yueqis
paper_authors._id: 67f71c8ff13d9b7b2194b778
paper_authors.name: Yu Gu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-13T19:25:50.246Z
paper_authors._id: 5e7e595230dc073f817a2bb5
paper_authors.avatarUrl: /avatars/d5ff36e45555d9e169cf56c845736444.svg
paper_authors.fullname: Yu Gu
paper_authors.type: user
paper_authors.user: entslscheia
paper_authors._id: 67f71c8ff13d9b7b2194b779
paper_authors.name: Jayanth Srinivasa
paper_authors._id: 67f71c8ff13d9b7b2194b77a
paper_authors.name: Gaowen Liu
paper_authors._id: 67f71c8ff13d9b7b2194b77b
paper_authors.name: Graham Neubig
paper_authors._id: 67f71c8ff13d9b7b2194b77c
paper_authors.name: Yu Su
paper_title: SkillWeaver: Web Agents can Self-Improve by Discovering and Honing
    Skills
paper_summary: To survive and thrive in complex environments, humans have evolved
  sophisticated self-improvement mechanisms through environment exploration,
  hierarchical abstraction of experiences into reuseable skills, and
  collaborative construction of an ever-growing skill repertoire. Despite recent
  advancements, autonomous web agents still lack crucial self-improvement
  capabilities, struggling with procedural knowledge abstraction, refining
  skills, and skill composition. In this work, we introduce SkillWeaver, a
  skill-centric framework enabling agents to self-improve by autonomously
  synthesizing reusable skills as APIs. Given a new website, the agent
  autonomously discovers skills, executes them for practice, and distills
  practice experiences into robust APIs. Iterative exploration continually
  expands a library of lightweight, plug-and-play APIs, significantly enhancing
  the agent's capabilities. Experiments on WebArena and real-world websites
  demonstrate the efficacy of SkillWeaver, achieving relative success rate
  improvements of 31.8% and 39.8%, respectively. Additionally, APIs synthesized
  by strong agents substantially enhance weaker agents through transferable
  skills, yielding improvements of up to 54.3% on WebArena. These results
  demonstrate the effectiveness of honing diverse website interactions into APIs,
  which can be seamlessly shared among various web agents.
paper_ai_summary: SkillWeaver, a skill-centric framework, enables autonomous web agents to self-improve by synthesizing reusable skills as APIs, enhancing their capabilities and allowing skill transfer between agents.
paper_ai_keywords: SkillWeaver skill-centric framework autonomous synthesis reusable skills APIs iterative exploration WebArena skill transferability