SkillWeaver: Web Agents can Self-Improve by Discovering and Honing
  Skills
Authors: name: Boyuan Zheng, name: Michael Y. Fatemi, name: Xiaolong Jin, name: Zora Zhiruo Wang, name: Apurva Gandhi, name: Yueqi Song, name: Yu Gu, name: Jayanth Srinivasa, name: Gaowen Liu, name: Graham Neubig, name: Yu Su

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.