arXiv AI
WebXSkill: Skill Learning for Autonomous Web Agents
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arXiv AI
Jun 17
Beyond Domains: Reusing Web Skills via Transferable Interaction Patterns
arXiv:2606. 17645v1 Announce Type: new Abstract: Large language model (LLM) web agents are usually deployed as tool callers: each turn, the model reads a fresh page observation and emits one structured tool action.
arXiv AI
Jun 4
Online Skill Learning for Web Agents via State-Grounded Dynamic Retrieval
arXiv:2606. 04391v1 Announce Type: new Abstract: Language agents increasingly rely on reusable skills to improve multi-step web automation across related tasks.
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.
arXiv AI
Aug 10
WebGrader: Training LLMs for Web Development with Self-Evolving Programmatic Grader
arXiv:2608. 06474v1 Announce Type: new Abstract: Large language models increasingly generate complete websites from natural-language descriptions, and reinforcement learning has become a central approach to closing their remaining functional gap.
arXiv AI
Jun 2
MMG2Skill: Can Agents Distill In-the-Wild Guides into Self-Evolving Skills?
arXiv:2606. 01993v1 Announce Type: cross Abstract: Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks.
arXiv AI
Jul 29
HiSkill: Empowering LLM Agents with Hierarchical Skill Graphs
arXiv:2607. 25853v1 Announce Type: new Abstract: Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks.