构建了最大规模真实网页交互数据集，助力可复现的网页智能体研究。
WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces
- 通过视觉、结构、动作三重对齐，实现多模态数据标注
- 包含31,725条轨迹共31.8万步，覆盖复杂高价值任务
- 提出双阶段训练方法，在多个基准上达领先性能
我们提出了WebChain，目前最大的开源人类标注网页交互轨迹数据集，旨在加速网页智能体的可复现研究。该数据集包含31,725条轨迹，共31.8万步，核心特征是视觉、结构与动作数据的三重对齐，提供丰富的多模态监督信号。数据通过可扩展的采集管道获得，有效覆盖合成方法常遗漏的复杂高价值任务。基于此数据集，我们提出一种双阶段中段训练方案，将空间定位与规划解耦，在自建的WebChainBench及其他公开GUI基准上取得当前最优性能。本工作为构建和严谨评估下一代可扩展网页智能体提供了必要数据与洞见。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。