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About this paper
Yifan Wu, Yiran Peng, Yiyu Chen, Jianhao Ruan, Zijie Zhuang, Cheng Yang, Jiayi Zhang, Man Chen and 7 more
AutoWebWorld introduces a framework for synthesizing verifiable web environments using Finite State Machines and large language models as coding agents. This approach enables the scalable, low-cost generation of intrinsically verified training data, leading to state-of-the-art performance for autonomous Web GUI agents on real-world benchmarks.
Key insights
- Representing web environments with explicit FSM-based semantic states and deterministic transitions allows for programmatic, intrinsic verification, thereby overcoming the critical 'verifier bottleneck'.
- Advanced large language models can function as effective 'coding agents' capable of autonomously designing and building complex digital artifacts like web environments from abstract specifications.
- Synthetic, intrinsically verified data, despite being procedurally generated, effectively generalizes to complex real-world web interaction tasks and demonstrates a clear scaling law for improving agent performance.
Summary by alphaXiv.
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