WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos
Understanding affective dynamics in real-world social systems is fundamental to modeling and analyzing human-human interactions in complex environments. Group affect emerges from intertwined human-human interactions, con…
InfiniHuman: Infinite 3D Human Creation with Precise Control
Generating realistic and controllable 3D human avatars is a long-standing challenge, particularly when covering broad attribute ranges such as ethnicity, age, clothing styles, and detailed body shapes. Capturing and anno…
Image GenerationDistilling Human-Aligned Privacy Sensitivity Assessment from Large Language Models
Accurate privacy evaluation of textual data remains a critical challenge in privacy-preserving natural language processing. Recent work has shown that large language models (LLMs) can serve as reliable privacy evaluators…
UMDFaces: An Annotated Face Dataset for Training Deep Networks
Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets. However, most of the large data…
Face DetectionFace RecognitionKeypoint DetectionCheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch
Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. However, most RM research is centered on English and relies heavily on synthetic resources, which leads to limited and les…