WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces
This dataset and method address the problem of building and evaluating scalable web agents by providing rich, multi-modal human-annotated data for researchers.
This paper introduces WebChain, a large-scale dataset of 31,725 human-annotated web interaction trajectories and 318k steps, designed for web agent research. Utilizing this dataset, the authors propose a Dual Mid-Training recipe that achieves state-of-the-art performance on WebChainBench and other public GUI benchmarks.
We introduce WebChain, the largest open-source dataset of human-annotated trajectories on real-world websites, designed to accelerate reproducible research in web agents. It contains 31,725 trajectories and 318k steps, featuring a core Triple Alignment of visual, structural, and action data to provide rich, multi-modal supervision. The data is collected via a scalable pipeline that ensures coverage of complex, high-value tasks often missed by synthetic methods. Leveraging this dataset, we propose a Dual Mid-Training recipe that decouples spatial grounding from planning, achieving state-of-the-art performance on our proposed WebChainBench and other public GUI benchmarks. Our work provides the data and insights necessary to build and rigorously evaluate the next generation of scalable web agents.