AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning

• URL: http://arxiv.org/abs/2506.01391v2
• Date: Tue, 17 Jun 2025 02:57:04 GMT
• Title: AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning
• Authors: Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Jie Xie, Wei Zhou, Wang Xu, Yuanheng Zhang, Zhou Su, Zhongwu Zhai, Xiaoming Liu, Yudong Mei, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun,
• Abstract summary: AgentCPM-GUI is built for robust and efficient on-device GUI interaction.<n>Our training pipeline includes grounding-aware pre-training to enhance perception.<n>AgentCPM-GUI achieves state-of-the-art performance on five public benchmarks.
• Score: 82.42421823672954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent progress of large language model agents has opened new possibilities for automating tasks through graphical user interfaces (GUIs), especially in mobile environments where intelligent interaction can greatly enhance usability. However, practical deployment of such agents remains constrained by several key challenges. Existing training data is often noisy and lack semantic diversity, which hinders the learning of precise grounding and planning. Models trained purely by imitation tend to overfit to seen interface patterns and fail to generalize in unfamiliar scenarios. Moreover, most prior work focuses on English interfaces while overlooks the growing diversity of non-English applications such as those in the Chinese mobile ecosystem. In this work, we present AgentCPM-GUI, an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. Our training pipeline includes grounding-aware pre-training to enhance perception, supervised fine-tuning on high-quality Chinese and English trajectories to imitate human-like actions, and reinforcement fine-tuning with GRPO to improve reasoning capability. We also introduce a compact action space that reduces output length and supports low-latency execution on mobile devices. AgentCPM-GUI achieves state-of-the-art performance on five public benchmarks and a new Chinese GUI benchmark called CAGUI, reaching $96.9\%$ Type-Match and $91.3\%$ Exact-Match. To facilitate reproducibility and further research, we publicly release all code, model checkpoint, and evaluation data.

Related papers

• MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI Agents [88.35544552383581]
  We introduce MMBench-GUI, a hierarchical benchmark for evaluating GUI automation agents across Windows, Linux, iOS, Android, and Web platforms.<n>It comprises four levels: GUI Content Understanding, Element Grounding, Task Automation, and Task Collaboration, covering essential skills for GUI agents.
  arXiv  Detail & Related papers  (2025-07-25T17:59:26Z)
• MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environment [63.62778707277929]
  MobileGUI-RL is a scalable framework that trains GUI agent in online environment.<n>It synthesizes a curriculum of learnable tasks through self-exploration and filtering.<n>It adapts GRPO to GUI navigation with trajectory-aware advantages and composite rewards.
  arXiv  Detail & Related papers  (2025-07-08T07:07:53Z)
• R-VLM: Region-Aware Vision Language Model for Precise GUI Grounding [18.100091500983044]
  A critical challenge in GUI automation is the precise grounding of interface elements across diverse platforms.<n>Existing vision-only GUI agents directly ground elements from large and cluttered screenshots.<n>We introduce R-VLM, a novel GUI grounding approach that leverages zoomed-in region proposals for precise element localization.
  arXiv  Detail & Related papers  (2025-07-08T04:56:57Z)
• Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation [83.92224427735859]
  We introduce a pre-operative critic mechanism that provides effective feedback prior to the actual execution.<n>We develop a reasoning-bootstrapping based data collection pipeline to create a GUI-Critic-Train and a GUI-Critic-Test.<n>Our model offers significant advantages in critic accuracy compared to current MLLMs.
  arXiv  Detail & Related papers  (2025-06-05T04:12:36Z)
• InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection [38.833925781308665]
  We introduce textitInfiGUIAgent, an MLLM-based GUI Agent trained with a two-stage supervised fine-tuning pipeline.<n> Stage 1 enhances fundamental skills such as GUI understanding and grounding, while Stage 2 integrates hierarchical reasoning and expectation-reflection reasoning skills.<n>textitInfiGUIAgent achieves competitive performance on several GUI benchmarks.
  arXiv  Detail & Related papers  (2025-01-08T15:45:21Z)
• Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction [69.57190742976091]
  Aguvis is a vision-based framework for autonomous GUI agents.<n>It standardizes cross-platform interactions and incorporates structured reasoning via inner monologue.<n>It achieves state-of-the-art performance across offline and real-world online benchmarks.
  arXiv  Detail & Related papers  (2024-12-05T18:58:26Z)
• ShowUI: One Vision-Language-Action Model for GUI Visual Agent [80.50062396585004]
  Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. We develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations. ShowUI, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding.
  arXiv  Detail & Related papers  (2024-11-26T14:29:47Z)
• CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation [61.68049335444254]
  Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments. We propose a Comprehensive Cognitive LLM Agent, CoCo-Agent, with two novel approaches, comprehensive environment perception (CEP) and conditional action prediction (CAP) With our technical design, our agent achieves new state-of-the-art performance on AITW and META-GUI benchmarks, showing promising abilities in realistic scenarios.
  arXiv  Detail & Related papers  (2024-02-19T08:29:03Z)
• ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation [30.693616802332745]
  This paper presents a novel benchmark, AssistGUI, to evaluate whether models are capable of manipulating the mouse and keyboard on the Windows platform in response to user-requested tasks. We propose an advanced Actor-Critic framework, which incorporates a sophisticated GUI driven by an AI agent and adept at handling lengthy procedural tasks.
  arXiv  Detail & Related papers  (2023-12-20T15:28:38Z)
• You Only Look at Screens: Multimodal Chain-of-Action Agents [37.118034745972956]
  Auto-GUI is a multimodal solution that directly interacts with the interface. We propose a chain-of-action technique to help the agent decide what action to execute. We evaluate our approach on a new device-control benchmark AITW with 30$K$ unique instructions.
  arXiv  Detail & Related papers  (2023-09-20T16:12:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.