@inproceedings{wu-etal-2025-reachagent,
    title = "{R}each{A}gent: Enhancing Mobile Agent via Page Reaching and Operation",
    author = "Wu, Qinzhuo  and
      Liu, Wei  and
      Luan, Jian  and
      Wang, Bin",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.244/",
    pages = "4760--4775",
    ISBN = "979-8-89176-189-6",
    abstract = "Recently, mobile AI agents have gained increasing attention. Given a task, mobile AI agents can interact with mobile devices in multiple steps and finally form a GUI flow that solves the task. However, existing agents tend to focus on most task-relevant elements at each step, leading to local optimal solutions and ignoring the overall GUI flow. To address this issue, we constructed a training dataset called MobileReach, which breaks the task into page reaching and operation subtasks. Furthermore, we propose ReachAgent, a two-stage framework that focuses on improving its task-completion abilities. It utilizes the page reaching and page operation subtasks, along with reward-based preference GUI flows, to further enhance the agent. Experimental results show that ReachAgent significantly improves the Intersection over Union (IoU) Accuracy and Text Accuracy by 7.12{\%} and 7.69{\%} on the step-level and 4.72{\%} and 4.63{\%} on the task-level compared to the SOTA agent. Our data and code will be released upon acceptance."
}