💡 The paper introduces MobileGym, a browser-based mobile environment designed for mobile GUI agent research. The main problem addressed is the lack of a verifiable and highly parallel simulation platform for training and evaluating mobile GUI agents. Traditional methods are limited by their inability to provide deterministic outcome signals and scalable reinforcement learning.

The authors propose MobileGym as a solution, which enables deterministic evaluation and scalable reinforcement learning through JSON-based state management and parallel execution. The platform captures the full environment state as structured JSON, allowing for easy configuration, forking, and comparison of states. This approach enables a single server to host hundreds of parallel instances, with low memory requirements and fast startup times.

MobileGym features a layered state model and a declarative task-definition framework, making it practical to create and program tasks at scale. The platform also includes a single programmatic judging mechanism that delivers both deterministic evaluation verdicts and dense RL rewards. To facilitate research, the authors provide MobileGym-Bench, a collection of 416 parameterized task templates across 28 apps, including 256 test and 160 train templates.

The results demonstrate the effectiveness of MobileGym in a Sim-to-Real case study, where a model trained in the simulation environment achieves a 12.8 percentage point gain on a 256-task test set. When executed on real devices, the model retains 95.1% of the simulation-side training gain, indicating the potential of MobileGym for real-world applications. Overall, MobileGym provides a verifiable and highly parallel simulation platform for mobile GUI agent research, enabling scalable reinforcement learning and deterministic evaluation.