ByteDance-Seed/UI-TARS-7B-DPO
UI-TARS-7B-DPO
UI-TARS-2B-SFT | UI-TARS-7B-SFT | **UI-TARS-7B-DPO**(Recommended) | UI-TARS-72B-SFT | **UI-TARS-72B-DPO**(Recommended)
Introduction
UI-TARS is a next-generation native GUI agent model designed to interact seamlessly with graphical user interfaces (GUIs) using human-like perception, reasoning, and action capabilities. Unlike traditional modular frameworks, UI-TARS integrates all key components—perception, reasoning, grounding, and memory—within a single vision-language model (VLM), enabling end-to-end task automation without predefined workflows or manual rules. <!-- --> <p align="center"> <img src="https://github.com/bytedance/UI-TARS/blob/main/figures/UI-TARS-vs-Previous-SOTA.png?raw=true" width="90%"/> <p> <p align="center"> <img src="https://github.com/bytedance/UI-TARS/blob/main/figures/UI-TARS.png?raw=true" width="90%"/> <p>
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This repository contains the model for the paper UI-TARS: Pioneering Automated GUI Interaction with Native Agents.
Code: https://github.com/bytedance/UI-TARS
Performance
Perception Capabilty Evaluation | Model | VisualWebBench | WebSRC | SQAshort | |---------------------------|---------------|---------|----------| | Qwen2-VL-7B | 73.3 | 81.8 | 84.9 | | Qwen-VL-Max | 74.1 | 91.1 | 78.6 | | Gemini-1.5-Pro | 75.4 | 88.9 | 82.2 | | UIX-Qwen2-7B | 75.9 | 82.9 | 78.8 | | Claude-3.5-Sonnet | 78.2 | 90.4 | 83.1 | | GPT-4o | 78.5 | 87.7 | 82.3 | | UI-TARS-2B | 72.9 | 89.2 | 86.4 | | UI-TARS-7B | 79.7 | 93.6 | 87.7 | | UI-TARS-72B | 82.8 | 89.3 | 88.6 |
Grounding Capability Evaluation
- ScreenSpot Pro
- ScreenSpot
- ScreenSpot v2
Offline Agent Capability Evaluation
- Multimodal Mind2Web
- Android Control and GUI Odyssey
Online Agent Capability Evaluation
Citation
If you find our paper and model useful in your research, feel free to give us a cite.
@article{qin2025ui,
title={UI-TARS: Pioneering Automated GUI Interaction with Native Agents},
author={Qin, Yujia and Ye, Yining and Fang, Junjie and Wang, Haoming and Liang, Shihao and Tian, Shizuo and Zhang, Junda and Li, Jiahao and Li, Yunxin and Huang, Shijue and others},
journal={arXiv preprint arXiv:2501.12326},
year={2025}
}