title: UI-R1: Enhancing Action Prediction of GUI Agents by Reinforcement
    Learning
summary: 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.
paper_authors._id: 67e606fb6c44ab0376a498a1
paper_authors.name: Zhengxi Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T08:42:01.155Z
paper_authors._id: 676127cf11b19ea602bb202a
paper_authors.avatarUrl: /avatars/dfd802a24bd63e509728159ebb1769f6.svg
paper_authors.fullname: Zhengxi Lu
paper_authors.type: user
paper_authors.user: LZXzju
paper_authors._id: 67e606fb6c44ab0376a498a2
paper_authors.name: Yuxiang Chai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-28T08:37:39.852Z
paper_authors._id: 6458ce236fa580137af5aa95
paper_authors.avatarUrl: /avatars/db65a7332e375eb5daad5c1b076b1e3b.svg
paper_authors.fullname: Yuxiang Chai
paper_authors.type: user
paper_authors.user: Yuxiang007
paper_authors._id: 67e606fb6c44ab0376a498a3
paper_authors.name: Yaxuan Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T08:42:12.269Z
paper_authors._id: 65c237220c57a7141888363e
paper_authors.avatarUrl: /avatars/ce43c52f47d524c5b747523058946325.svg
paper_authors.fullname: guoyaxuan
paper_authors.type: user
paper_authors.user: guoyaxuan0106
paper_authors._id: 67e606fb6c44ab0376a498a4
paper_authors.name: Xi Yin
paper_authors._id: 67e606fb6c44ab0376a498a5
paper_authors.name: Liang Liu
paper_authors._id: 67e606fb6c44ab0376a498a6
paper_authors.name: Hao Wang
paper_authors._id: 67e606fb6c44ab0376a498a7
paper_authors.name: Guanjing Xiong
paper_authors._id: 67e606fb6c44ab0376a498a8
paper_authors.name: Hongsheng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T08:43:30.298Z
paper_authors._id: 65c04e9c27a5fdca81abcbd9
paper_authors.avatarUrl: /avatars/12a155683c824fa23da4a9e2bed4f64e.svg
paper_authors.fullname: Hongsheng LI
paper_authors.type: user
paper_authors.user: hsli-cuhk
paper_title: UI-R1: Enhancing Action Prediction of GUI Agents by Reinforcement
    Learning
paper_summary: 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.
paper_ai_summary: Rule-based reinforcement learning enhances multimodal large language models for GUI action prediction, improving data efficiency and performance.
paper_ai_keywords: reinforcement learning multimodal large language models GUI action prediction Group Relative Policy Optimization policy-based algorithms AndroidControl ScreenSpot-Pro supervised fine-tuning