arXiv AI
Training Needs Trustworthy Worlds: Verified Synthetic Web Environments for Agent Learning
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WebHarbor: Docking Real Websites for Evolving GUI Agent Environments
WebHarbor is a collection of 153 dockerized website environments that faithfully replicate real-world web services, offering 3,518 tasks that include multi-step and post-authentication workflows. Built through task-driven human-agent collaboration, the suite ensures deterministic control, visual fidelity, and functional correctness. The platform enables reliable evaluation and cost-efficient reinforcement learning, with a Qwen 4B-level GUI agent trained in WebHarbor generalizing well to unseen tasks and matching leading open-source agents on four benchmarks.
arXiv AI
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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
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InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training
arXiv:2601. 04126v3 Announce Type: replace-cross Abstract: GUI agents that interact with graphical interfaces on behalf of users represent a promising direction for practical AI assistants.
arXiv AI
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WebXSkill: Skill Learning for Autonomous Web Agents
arXiv:2604.13318v2 Announce Type: replace Abstract: Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap...
arXiv AI
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ScaleWoB: Guiding GUI Agents with Coding Agents via Large-Scale Environmental Synthesis
arXiv:2605. 25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments.
arXiv AI
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OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents
arXiv:2606. 02031v1 Announce Type: cross Abstract: Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites.