ZeroGUI: Automating Online GUI Learning at Zero Human Cost
Authors: name: Chenyu Yang, name: Shiqian Su, name: Shi Liu, name: Xuan Dong, name: Yue Yu, name: Weijie Su, name: Xuehui Wang, name: Zhaoyang Liu, name: Jinguo Zhu, name: Hao Li, name: Wenhai Wang, name: Yu Qiao, name: Xizhou Zhu, name: Jifeng Dai

The rapid advancement of large Vision-Language Models (VLMs) has propelled
the development of pure-vision-based GUI Agents, capable of perceiving and
operating Graphical User Interfaces (GUI) to autonomously fulfill user
instructions. However, existing approaches usually adopt an offline learning
framework, which faces two core limitations: (1) heavy reliance on high-quality
manual annotations for element grounding and action supervision, and (2)
limited adaptability to dynamic and interactive environments. To address these
limitations, we propose ZeroGUI, a scalable, online learning framework for
automating GUI Agent training at Zero human cost. Specifically, ZeroGUI
integrates (i) VLM-based automatic task generation to produce diverse training
goals from the current environment state, (ii) VLM-based automatic reward
estimation to assess task success without hand-crafted evaluation functions,
and (iii) two-stage online reinforcement learning to continuously interact with
and learn from GUI environments. Experiments on two advanced GUI Agents
(UI-TARS and Aguvis) demonstrate that ZeroGUI significantly boosts performance
across OSWorld and AndroidLab environments. The code is available at
https://github.com/OpenGVLab/ZeroGUI.