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

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.