UI-Genie-Agent-7B

This model is presented in UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents.

Code: https://github.com/Euphoria16/UI-Genie

Model Description

UI-Genie-Agent-7B is a state-of-the-art Multimodal Large Language Model specifically trained for mobile GUI automation tasks. It is part of the UI-Genie framework, which introduces a novel self-improving approach for enhancing MLLM-based mobile GUI agents through iterative agent-reward model co-evolution.

This model achieves state-of-the-art performance on mobile GUI benchmarks by eliminating the need for manual annotation through synthetic trajectory generation guided by our specialized reward model UI-Genie-RM.

Model Architecture

• Base Model: Qwen2.5-VL-7B-Instruct
• Training Method: Supervised fine-tuning with exisiting trajetory datasets and our synthetic trajectory data
• Action Space Coverage: Supports comprehensive mobile interactions (click, swipe, type, etc.) and Set-of-Mark mode.

Performance

AndroidControl Benchmark

AndroidLab Benchmark

Android Arena (A3) Benchmark

Training Data

Our model is trained on a combination of:

• AndroidControl: 15.3K trajectories (high & low level tasks)
• AMEX: 2.9K trajectories (high-level tasks)
• AndroidLab: 726 trajectories (high-level tasks)
• UI-Genie-Agent-16k: 2.2K synthetic trajectories (our generated data)

Action Space

The model supports a comprehensive action space for mobile interactions:

Citation

@misc{xiao2025uigenieselfimprovingapproachiteratively,
      title={UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents}, 
      author={Han Xiao and Guozhi Wang and Yuxiang Chai and Zimu Lu and Weifeng Lin and Hao He and Lue Fan and Liuyang Bian and Rui Hu and Liang Liu and Shuai Ren and Yafei Wen and Xiaoxin Chen and Aojun Zhou and Hongsheng Li},
      year={2025},
      eprint={2505.21496},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.21496}, 
}