WebWorld: A Large-Scale World Model for Web Agent Training

Understand

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.