AutoWebWorld: Synthesizing Infinite Verifiable Web Environments via Finite State Machines

The performance of autonomous Web GUI agents heavily relies on the quality and quantity of training data. However, collecting high-quality interaction trajectories from real websites is expensive and difficult to verify. Specifically, the underlying state transitions of real websites are hidden from the agent: only rendered UI feedback (e.g., screenshots) is observable, while the true internal state remains inaccessible. This forces existing pipelines to rely on external verifiers, such as human annotators or LLM judges, which are both inconsistent and costly. To address this, we propose AutoWebWorld, a framework for synthesizing controllable and verifiable web environments by modeling them as Finite State Machines (FSMs) and using coding agents to translate FSMs into synthetic websites. Unlike real websites, where state transitions are implicit, AutoWebWorld explicitly defines all states, actions, and transition rules. This enables programmatic verification: action validity is enforced by FSM preconditions and deterministic transition rules, and task success is certified by reaching a goal state in the FSM graph. Our approach automates the data generation pipeline, generating over 11,663 verified trajectories from 29 synthetic websites at only $0.04 per trajectory. Training on this synthetic data significantly boosts real-world performance. Our 7B Web GUI agent outperforms all baselines within 15 steps on WebVoyager. Furthermore, we observe a clear scaling law: as the synthetic data volume increases, performance on WebVoyager and Online-Mind2Web consistently improves.