ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform
  Data
Authors: name: Zhaoyang Liu; name: JingJing Xie; name: Zichen Ding; name: Zehao Li; name: Bowen Yang; name: Zhenyu Wu; name: Xuehui Wang; name: Qiushi Sun; name: Shi Liu; name: Weiyun Wang; name: Shenglong Ye; name: Qingyun Li; name: Zeyue Tian; name: Gen Luo; name: Xiangyu Yue; name: Biqing Qi; name: Kai Chen; name: Bowen Zhou; name: Yu Qiao; name: Qifeng Chen; name: Wenhai Wang
Published: 2025-09-18 17:59:22

Abstract: Vision-Language Models (VLMs) have enabled computer use agents (CUAs) that
operate GUIs autonomously, showing great potential, yet progress is limited by
the lack of large-scale, open-source computer use data and foundation models.
In this work, we introduce ScaleCUA, a step toward scaling open-source CUAs. It
offers a large-scale dataset spanning 6 operating systems and 3 task domains,
built via a closed-loop pipeline uniting automated agents with human experts.
Trained on this scaled-up data, ScaleCUA can operate seamlessly across
platforms. Specifically, it delivers strong gains over baselines (+26.6 on
WebArena-Lite-v2, +10.7 on ScreenSpot-Pro) and sets new state-of-the-art
results (94.4% on MMBench-GUI L1-Hard, 60.6% on OSWorld-G, 47.4% on
WebArena-Lite-v2). These findings underscore the power of data-driven scaling
for general-purpose computer use agents. We will release data, models, and code
to advance future research: https://github.com/OpenGVLab/ScaleCUA.