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About this paper
Haiyang Xu, Xi Zhang, Haowei Liu, Junyang Wang, Zhaozai Zhu, Shengjie Zhou, Xuhao Hu, Feiyu Gao and 11 more
GUI-Owl-1.5, developed by Tongyi Lab at Alibaba Group, introduces a multi-platform GUI agent model that achieves state-of-the-art performance among open-source models across over 20 GUI benchmarks. This model demonstrates strong capabilities in GUI understanding, grounding, and execution across desktops, mobiles, and browsers, often rivaling or surpassing proprietary systems.
Key insights
- A hybrid data generation approach, combining synthetic challenging scenarios and scaled data augmentation, is essential for overcoming data scarcity in complex GUI environments.
- Explicitly training models for higher-order cognitive skills, such as GUI knowledge injection, world modeling, and Chain-of-Thought reasoning, significantly enhances an agent's long-horizon planning and memory capabilities.
- Reinforcement learning for multi-platform GUI agents requires specialized strategies like alternating multi-device optimization and robust handling of grouped rollouts to ensure training stability and cross-device generalization.
Summary by alphaXiv.
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