UI-R1: Enhancing Action Prediction of GUI Agents by Reinforcement
  Learning
Authors: name: Zhengxi Lu, name: Yuxiang Chai, name: Yaxuan Guo, name: Xi Yin, name: Liang Liu, name: Hao Wang, name: Guanjing Xiong, name: Hongsheng Li

The recent DeepSeek-R1 has showcased the emergence of reasoning capabilities
in LLMs through reinforcement learning (RL) with rule-based rewards. Building
on this idea, we are the first to explore how rule-based RL can enhance the
reasoning capabilities of multimodal large language models (MLLMs) for graphic
user interface (GUI) action prediction tasks. To this end, we curate a small
yet high-quality dataset of 136 challenging tasks, encompassing five common
action types on mobile devices. We also introduce a unified rule-based action
reward, enabling model optimization via policy-based algorithms such as Group
Relative Policy Optimization (GRPO). Experimental results demonstrate that our
proposed data-efficient model, UI-R1-3B, achieves substantial improvements on
both in-domain (ID) and out-of-domain (OOD) tasks. Specifically, on the ID
benchmark AndroidControl, the action type accuracy improves by 15%, while
grounding accuracy increases by 10.3%, compared with the base model (i.e.
Qwen2.5-VL-3B). On the OOD GUI grounding benchmark ScreenSpot-Pro, our model
surpasses the base model by 6.0% and achieves competitive performance with
larger models (e.g., OS-Atlas-7B), which are trained via supervised fine-tuning
(SFT) on 76K data. 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.