arXiv AI By Jianwei Zhang, Sihan Cao, Pengcheng Zheng, Ya Wen, Pei Ke, Kuien Liu, Shen Gao, Wei Dong, Yang Yang, Chaoning Zhang

When and What to Teach: Budget-Aware Online Adaptation for Web Agents

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Hugging Face Trending Papers
Jun 1

OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents

Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the strongest systems remain largely proprietary, while open agents still depend heavily on supervised post-training over large collections of curated web trajectories.

arXiv AI
Sep 10

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

SCAFFOLD is a self‑improving framework for visual web agents that automatically induces parametric, executable skills from successful trajectories and organizes them into a recursively composed hierarchy. It compresses the skill library using a minimum‑description‑length criterion and behavioral equivalence checks, and periodically distills these skills back into model weights to internalize the abstractions. Experiments on WebArena, VisualWebArena, and Online‑Mind2Web show that SCAFFOLD raises success rates by 11.1–17.2 absolute points over the best skill‑augmented baseline and continues to improve across five self‑improvement iterations without collapsing the library.

By Bowei He, Xiaokun Zhang, Meng Ding, Xue Liu