@inproceedings{gu-etal-2026-mobile,
    title = "Mobile-R1: Towards Interactive Capability for {VLM}-Based Mobile Agent via Systematic Training",
    author = "Gu, Jihao  and
      Ai, Qihang  and
      Wang, Yingyao  and
      Bu, Pi  and
      Xing, Jingxuan  and
      Cao, Yue  and
      Zhu, Zekun  and
      Jiang, Wei  and
      Wang, Ziming  and
      Zhao, Yingxiu  and
      Zhang, Ming-Liang  and
      Song, Jun  and
      Jiang, Yuning  and
      Zheng, Bo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1422/",
    pages = "30802--30820",
    ISBN = "979-8-89176-390-6",
    abstract = "Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinforcement learning paradigms like Group Relative Policy Optimization (GRPO). However, existing approaches centers on offline training or local action-level rewards often trap agents in local optima, hindering effective exploration and error correction with the environment. Crucially, we find that directly applying task-level rewards often leads to convergence difficulties due to the sparse nature of GUI interactions. To address these challenges, we present \textbf{Mobile-R1}, a systematic training recipe that bridges atomic action execution and strategic task completion. We propose a hierarchical curriculum consisting of three stages: (1) format alignment for reasoning structure, (2) on-policy exploration with verifiable action feedback to ground basic execution, and (3) multi-turn task-level training with realistic environment to unlock exploration and self-correction. This hierarchical strategy effectively bootstraps the agent, significantly enhancing its capability for exploration and self-correction (the ``Eureka'' moments). Furthermore, addressing the critical scarcity of diverse GUI data in non-English ecosystems, we contribute a comprehensive Chinese mobile dataset covering 28 applications with 24,521 high-quality manual annotations, and establish a rigorous benchmark with 500 trajectories. We will open source all resources, including the dataset, benchmark, model weight, and codes: \url{https://mobile-r1.github.io/Mobile-R1/}."
}