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[2602.14721] WebWorld: A Large-Scale World Model for Web Agent Training
Summary
WebWorld introduces a large-scale simulator for training web agents, utilizing over 1 million open-web interactions to enhance generalization and performance across various domains.
Why It Matters
This research addresses the limitations of traditional simulators by providing a scalable solution for training AI agents in real-world scenarios. The ability to generalize across different tasks and environments is crucial for advancing AI capabilities, making WebWorld a significant contribution to the field.
Key Takeaways
- WebWorld leverages 1M+ open-web interactions for training.
- It achieves performance comparable to advanced models like Gemini-3-Pro.
- The framework supports reasoning and long-horizon simulations.
- WebWorld-Bench introduces dual metrics for evaluation across nine dimensions.
- The model shows cross-domain generalization to code, GUI, and gaming environments.
Computer Science > Artificial Intelligence arXiv:2602.14721 (cs) [Submitted on 16 Feb 2026] Title:WebWorld: A Large-Scale World Model for Web Agent Training Authors:Zikai Xiao, Jianhong Tu, Chuhang Zou, Yuxin Zuo, Zhi Li, Peng Wang, Bowen Yu, Fei Huang, Junyang Lin, Zuozhu Liu View a PDF of the paper titled WebWorld: A Large-Scale World Model for Web Agent Training, by Zikai Xiao and 9 other authors View PDF HTML (experimental) Abstract:Web agents require massive trajectories to generalize, yet real-world training is constrained by network latency, rate limits, and safety risks. We introduce \textbf{WebWorld} series, the first open-web simulator trained at scale. While existing simulators are restricted to closed environments with thousands of trajectories, WebWorld leverages a scalable data pipeline to train on 1M+ open-web interactions, supporting reasoning, multi-format data, and long-horizon simulations of 30+ steps. For intrinsic evaluation, we introduce WebWorld-Bench with dual metrics spanning nine dimensions, where WebWorld achieves simulation performance comparable to Gemini-3-Pro. For extrinsic evaluation, Qwen3-14B trained on WebWorld-synthesized trajectories improves by +9.2\% on WebArena, reaching performance comparable to GPT-4o. WebWorld enables effective inference-time search, outperforming GPT-5 as a world model. Beyond web simulation, WebWorld exhibits cross-domain generalization to code, GUI, and game environments, providing a replicable recipe for world...