Generalization in Online Reinforcement Learning for Mobile Agents

• URL: http://arxiv.org/abs/2603.07432v1
• Date: Sun, 08 Mar 2026 03:08:11 GMT
• Title: Generalization in Online Reinforcement Learning for Mobile Agents
• Abstract summary: We introduce textbfAndroidWorld-Generalization, a benchmark for evaluating zero-shot generalization to unseen task instances, templates, and applications.<n>Experiments on AndroidWorld-Generalization show that RL enables a 7B- parameter VLM agent to surpass supervised fine-tuning baselines.<n>To support and fair comparison, we open-source the full RL training system, including the environment, task suite, models, prompt configurations, and the underlying infrastructure.
• Score: 32.98335803990582
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Graphical user interface (GUI)-based mobile agents automate digital tasks on mobile devices by interpreting natural-language instructions and interacting with the screen. While recent methods apply reinforcement learning (RL) to train vision-language-model(VLM) agents in interactive environments with a primary focus on performance, generalization remains underexplored due to the lack of standardized benchmarks and open-source RL systems. In this work, we formalize the problem as a Contextual Markov Decision Process (CMDP) and introduce \textbf{AndroidWorld-Generalization}, a benchmark with three increasingly challenging regimes for evaluating zero-shot generalization to unseen task instances, templates, and applications. We further propose an RL training system that integrates Group Relative Policy Optimization (GRPO) with a scalable rollout collection system, consisting of containerized infrastructure and asynchronous execution % , and error recovery to support reliable and efficient training. Experiments on AndroidWorld-Generalization show that RL enables a 7B-parameter VLM agent to surpass supervised fine-tuning baselines, yielding a 26.1\% improvement on unseen instances but only limited gains on unseen templates (15.7\%) and apps (8.3\%), underscoring the challenges of generalization. As a preliminary step, we demonstrate that few-shot adaptation at test-time improves performance on unseen apps, motivating future research in this direction. To support reproducibility and fair comparison, we open-source the full RL training system, including the environment, task suite, models, prompt configurations, and the underlying infrastructure \footnote{https://github.com/zihuanjiang/AndroidWorld-Generalization}.

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