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[2602.14296] AutoWebWorld: Synthesizing Infinite Verifiable Web Environments via Finite State Machines
Summary
The paper presents AutoWebWorld, a framework that synthesizes verifiable web environments using Finite State Machines, enhancing the training of autonomous web GUI agents.
Why It Matters
AutoWebWorld addresses the challenges of collecting reliable training data for web-based AI agents, offering a cost-effective solution that improves performance through synthetic data generation. This innovation is crucial for advancing AI capabilities in web interaction.
Key Takeaways
- AutoWebWorld synthesizes web environments as Finite State Machines for better training data.
- The framework allows for programmatic verification of actions, enhancing reliability.
- Synthetic data generation is cost-effective, producing over 11,663 verified trajectories.
- Training on this synthetic data significantly improves real-world performance of AI agents.
- A clear scaling law indicates that increasing synthetic data volume boosts performance.
Computer Science > Artificial Intelligence arXiv:2602.14296 (cs) [Submitted on 15 Feb 2026] Title:AutoWebWorld: Synthesizing Infinite Verifiable Web Environments via Finite State Machines Authors:Yifan Wu, Yiran Peng, Yiyu Chen, Jianhao Ruan, Zijie Zhuang, Cheng Yang, Jiayi Zhang, Man Chen, Yenchi Tseng, Zhaoyang Yu, Liang Chen, Yuyao Zhai, Bang Liu, Chenglin Wu, Yuyu Luo View a PDF of the paper titled AutoWebWorld: Synthesizing Infinite Verifiable Web Environments via Finite State Machines, by Yifan Wu and 14 other authors View PDF HTML (experimental) Abstract:The performance of autonomous Web GUI agents heavily relies on the quality and quantity of their training data. However, a fundamental bottleneck persists: collecting interaction trajectories from real-world websites is expensive and difficult to verify. The underlying state transitions are hidden, leading to reliance on inconsistent and costly external verifiers to evaluate step-level correctness. To address this, we propose AutoWebWorld, a novel framework for synthesizing controllable and verifiable web environments by modeling them as Finite State Machines (FSMs) and use coding agents to translate FSMs into interactive websites. Unlike real websites, where state transitions are implicit, AutoWebWorld explicitly defines all states, actions, and transition rules. This enables programmatic verification: action correctness is checked against predefined rules, and task success is confirmed by reaching a goal state in ...
