SkillWeaver: Web Agents can Self-Improve by Discovering and Honing
  Skills
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.
67f71c8ff13d9b7b2194b772
Boyuan Zheng
67f71c8ff13d9b7b2194b773
Michael Y. Fatemi
67f71c8ff13d9b7b2194b774
Xiaolong Jin
67f71c8ff13d9b7b2194b775
Zora Zhiruo Wang
67f71c8ff13d9b7b2194b776
Apurva Gandhi
67f71c8ff13d9b7b2194b777
Yueqi Song
claimed_verified
2025-04-17T08:05:29.717Z
63e7bf7be02ee67e8e53f78d
https://cdn-avatars.huggingface.co/v1/production/uploads/63e7bf7be02ee67e8e53f78d/z9j1PWrurEyIA2JXzpz0i.png
Yueqi Song
user
yueqis
67f71c8ff13d9b7b2194b778
Yu Gu
claimed_verified
2025-04-13T19:25:50.246Z
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/avatars/d5ff36e45555d9e169cf56c845736444.svg
Yu Gu
user
entslscheia
67f71c8ff13d9b7b2194b779
Jayanth Srinivasa
67f71c8ff13d9b7b2194b77a
Gaowen Liu
67f71c8ff13d9b7b2194b77b
Graham Neubig
67f71c8ff13d9b7b2194b77c
Yu Su
SkillWeaver: Web Agents can Self-Improve by Discovering and Honing
  Skills
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.
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.
SkillWeaver skill-centric framework autonomous synthesis reusable skills APIs iterative exploration WebArena skill transferability