SkillWeaver：網絡代理能通過發現與精進技能實現自我提升

摘要

為了在複雜環境中生存並茁壯成長，人類已演化出精密的自我提升機制，這些機制包括環境探索、將經驗分層抽象化為可重複使用的技能，以及協作構建不斷增長的技能庫。儘管近期取得了進展，自主網絡代理仍缺乏關鍵的自我提升能力，在程序性知識的抽象、技能的精煉及技能組合方面面臨挑戰。在本研究中，我們引入了SkillWeaver，這是一個以技能為中心的框架，使代理能夠通過自主合成可重複使用的API來實現自我提升。面對新網站時，代理自主發現技能，執行這些技能進行練習，並將練習經驗提煉成穩健的API。通過迭代探索，不斷擴展輕量級、即插即用的API庫，顯著增強了代理的能力。在WebArena和真實世界網站上的實驗證明了SkillWeaver的有效性，分別實現了31.8%和39.8%的相對成功率提升。此外，由強大代理合成的API通過可轉移技能大幅提升了較弱代理的能力，在WebArena上最高提升了54.3%。這些結果表明，將多樣化的網站互動精煉成API並在不同網絡代理間無縫共享，具有顯著的成效。

English

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：網絡代理能通過發現與精進技能實現自我提升

SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills

摘要

Summary

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