UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909621777596416 |
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| author | Lu, Zhengxi Chai, Yuxiang Guo, Yaxuan Yin, Xi Liu, Liang Wang, Hao Xiao, Han Ren, Shuai Xiong, Guanjing Li, Hongsheng |
| author_facet | Lu, Zhengxi Chai, Yuxiang Guo, Yaxuan Yin, Xi Liu, Liang Wang, Hao Xiao, Han Ren, Shuai Xiong, Guanjing Li, Hongsheng |
| contents | The recent DeepSeek-R1 has showcased the emergence of reasoning capabilities in LLMs through reinforcement learning (RL) with rule-based rewards. Despite its success in language models, its application in multi-modal domains, particularly in graphic user interface (GUI) agent tasks, remains under-explored. To address this issue, we propose UI-R1, the first framework to explore how rule-based RL can enhance the reasoning capabilities of multimodal large language models (MLLMs) for GUI action prediction tasks. Specifically, UI-R1 introduces a novel rule-based action reward, enabling model optimization via policy-based algorithms such as Group Relative Policy Optimization (GRPO). For efficient training, we curate a small yet high-quality dataset of 136 challenging tasks, encompassing five common action types on mobile devices. Experimental results demonstrate that our proposed UI-R1-3B achieves significant improvements over the base model (i.e. Qwen2.5-VL-3B) on both in-domain (ID) and out-of-domain (OOD) tasks, with average accuracy gains of 22.1% on ScreenSpot, 6.0% on ScreenSpot-Pro, and 12.7% on ANDROIDCONTROL. Furthermore, UI-R1-3B delivers competitive performance compared to larger models (e.g., OS-Atlas-7B) trained via supervised fine-tuning (SFT) on 76K samples. We additionally develop an optimized version, UI-R1-E-3B, which significantly improves both grounding efficiency and accuracy. These results underscore the potential of rule-based reinforcement learning to advance GUI understanding and control, paving the way for future research in this domain. Code website: https://github.com/lll6gg/UI-R1. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning Lu, Zhengxi Chai, Yuxiang Guo, Yaxuan Yin, Xi Liu, Liang Wang, Hao Xiao, Han Ren, Shuai Xiong, Guanjing Li, Hongsheng Artificial Intelligence The recent DeepSeek-R1 has showcased the emergence of reasoning capabilities in LLMs through reinforcement learning (RL) with rule-based rewards. Despite its success in language models, its application in multi-modal domains, particularly in graphic user interface (GUI) agent tasks, remains under-explored. To address this issue, we propose UI-R1, the first framework to explore how rule-based RL can enhance the reasoning capabilities of multimodal large language models (MLLMs) for GUI action prediction tasks. Specifically, UI-R1 introduces a novel rule-based action reward, enabling model optimization via policy-based algorithms such as Group Relative Policy Optimization (GRPO). For efficient training, we curate a small yet high-quality dataset of 136 challenging tasks, encompassing five common action types on mobile devices. Experimental results demonstrate that our proposed UI-R1-3B achieves significant improvements over the base model (i.e. Qwen2.5-VL-3B) on both in-domain (ID) and out-of-domain (OOD) tasks, with average accuracy gains of 22.1% on ScreenSpot, 6.0% on ScreenSpot-Pro, and 12.7% on ANDROIDCONTROL. Furthermore, UI-R1-3B delivers competitive performance compared to larger models (e.g., OS-Atlas-7B) trained via supervised fine-tuning (SFT) on 76K samples. We additionally develop an optimized version, UI-R1-E-3B, which significantly improves both grounding efficiency and accuracy. These results underscore the potential of rule-based reinforcement learning to advance GUI understanding and control, paving the way for future research in this domain. Code website: https://github.com/lll6gg/UI-R1. |
| title | UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.21620 |