SCAFFOLD: Self-Improving Web Agents via Recursive Parametric Skill Abstraction

Abstract:Web agents need to navigate visually rich, long-horizon interfaces that change across sites, yet most previous agents still learn each task in isolation and discard the procedural knowledge they accumulate. Recent skill-augmented frameworks take an important first step, but they treat the skill library as a flat or two-tier prompt-side cache and offer no principled mechanism for compressing redundancy or composing skills recursively. We introduce \textsc{Scaffold}, a self-improving framework for visual web agents that (i) induces parametric, executable skills from successful trajectories under a multi-instance abstraction constraint, (ii) maintains a recursively composed hierarchy in which higher-level skills invoke lower-level ones, (iii) compacts the library via a minimum-description-length (MDL) criterion and behavioral equivalence checking, and (iv) periodically distills skill-augmented trajectories back into model weights to internalize the abstractions. Across WebArena, VisualWebArena, and a held-out split of Online-Mind2Web, \textsc{Scaffold} improves success rate by $11.1$--$17.2$ absolute points over the strongest skill-augmented baseline and shows monotonic gains across five self-improvement iterations without library collapse. We release the code and documents in the Github \href{this https URL}{repository}.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.05511 [cs.AI]

(or arXiv:2609.05511v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.05511 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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