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

• URL: http://arxiv.org/abs/2609.05511v1
• Date: Mon, 31 Aug 2026 08:16:56 GMT
• Title: SCAFFOLD: Self-Improving Web Agents via Recursive Parametric Skill Abstraction
• Abstract summary: textscScaffold is a self-improving framework for visual web agents.<n>It induces parametric, executable skills from successful trajectories under a multi-instance abstraction constraint.<n>textscScaffold improves success rate by $11.1$--$17.2$ absolute points over the strongest skill-augmented baseline.
• Score: 13.7368753473801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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{https://github.com/BokwaiHo/SCAFFOLD}{repository}.

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