让手机界面智能体更高效，自动适应任务难易度。
MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agents
- 根据任务难度动态调整训练策略，提升学习稳定性。
- 在AndroidWorld上达80.2%成功率，AndroidLab上53.6%。
- 适合想提升手机自动化智能体性能的研究者与开发者。
随着视觉语言模型的发展，构建通用图形界面（GUI）智能体变得愈发可行。然而，基于强化学习（RL）开发有效的移动端GUI智能体仍面临挑战，主要源于任务难度的长尾分布及大规模环境采样的低效性。本文提出在线代理强化学习框架MobileRL，其核心为难度自适应的GRPO算法（ADAGRPO）。ADAGRPO通过设计难度自适应正向回放与失败课程过滤机制，使模型能适应不同难度任务；引入最短路径奖励调节策略，优化多轮代理任务中的奖励分布。这些策略协同作用，显著提升训练稳定性与样本效率，在多种移动端应用和任务中表现优异。我们将MobileRL应用于两个开源模型（Qwen2.5-VL-7B-Instruct 和 GLM-4.1V-9B-Base），所得MOBILERL-9B模型在AndroidWorld上成功率达80.2%，在AndroidLab上达53.6%，达到当前最优水平。MobileRL框架已开源：https://github.com/THUDM/MobileRL。
原文摘要 · Abstract (English)
Building general-purpose graphical user interface (GUI) agents has become increasingly promising with the progress in vision language models. However, developing effective mobile GUI agents with reinforcement learning (RL) remains challenging due to the heavy-tailed distribution of task difficulty and the inefficiency of large-scale environment sampling. We present an online agentic reinforcement learning framework MobileRL to enhance GUI agents in mobile environments. Its core component is the Difficulty-ADAptive GRPO (ADAGRPO) algorithm. In ADAGRPO, we design difficulty-adaptive positive replay and failure curriculum filtering to adapt the model to different task difficulties. We introduce the shortest-path reward adjustment strategy to reshape rewards concerning the task length in multi-turn agentic tasks. Those strategies jointly stabilize RL training, improve sample efficiency, and generate strong performance across diverse mobile apps and tasks. We apply MOBILERL to two open models (Qwen2.5-VL-7B-Instruct and GLM-4.1V-9B-Base). The resultant MOBILERL-9B model achieves state-of-the-art results in terms of success rates on both AndroidWorld (80.2%) and AndroidLab (53.6%). The MOBILERL framework is open-sourced at: https://github.com/THUDM/MobileRL.
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