MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research
2026-05-25 • Artificial Intelligence
Artificial IntelligenceComputation and Language
AI summaryⓘ
The authors created MobileGym, a lightweight environment that runs in web browsers to help apps learn and improve by practicing tasks. It captures app states in a simple, structured format and allows many copies to run at once, making it cheaper and faster for training. MobileGym includes many test tasks and uses a clear judging system to measure performance accurately. In tests, the authors showed that training in this simulated setup transfers well to real devices. This helps developers train apps on everyday mobile tasks without needing complex backends.
reinforcement learningdeterministic judgingstructured JSONsim-to-real transferparallel rolloutstask templatesstate-based environmentMobileGymonline RLbrowsers
Authors
Dingbang Wu, Rui Hao, Haiyang Wang, Shuzhe Wu, Han Xiao, Zhenghong Li, Bojiang Zhou, Zheng Ju, Zichen Liu, Lue Fan, Zhaoxiang Zhang
Abstract
We present MobileGym, a browser-hosted, lightweight, fully controllable environment for everyday mobile use, targeting interaction fidelity without replicating proprietary backends. It enables two capabilities previously out of reach for everyday apps: verifiable outcome signals through deterministic state-based judging over structured JSON state, and scalable online RL through low-cost parallel rollouts. The full environment state is captured, configured, forked, and compared as structured JSON, and a single server can host hundreds of parallel instances, with about 400 MB memory per instance and about 3 s cold start. A layered state model and a declarative task-definition framework keep state programmability and task creation practical at scale, and a single programmatic judging mechanism delivers both deterministic evaluation verdicts and dense RL rewards. The accompanying MobileGym-Bench provides 416 parameterized task templates, including 256 test and 160 train templates, over 28 apps, with deterministic judges and a structured AnswerSheet protocol that avoids free-text matching failures. In a Sim-to-Real case study, GRPO on Qwen3-VL-4B-Instruct gains +12.8 percentage points on the 256-task test set, and on a 59-task real-device signal subset, real-device execution retains 95.1% of the simulation-side training gain. Project page: https://mobilegym.github.io.