titleMAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environments
authorsPanrong Tong, Quyu Kong, Pengxiang Li, Tianyu Xia, Hanzhang Zhou, Lei Wang, Chenglin Cai, Long Li, Long Chen, Jianan Zhang, Zhengwen Zeng, Xu Zhang, Gongjie Zhang, Yue Wang, Steven Hoi
date2026-10-14
venuearXiv (preprint, not peer reviewed)
idarXiv:2610.20939
affiliationTongyi-MAI
upvotes56 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractOnline reinforcement learning (RL) with verifiable rewards has driven rapid progress in mathematics, coding, and tool use, but scaling this success to GUI agents remains challenging due to the high cost of interactive environments and the scarcity of realistic training tasks. We introduce MAI-Gym, an open all-in-one platform that unifies GUI agent evaluation, data collection, and online RL training across mobile, computer, and web environments. Through a shared rollout pipeline, MAI-Gym ensures consistent agent behavior across evaluation and training, supports ten established benchmarks, and scales to 20,000 parallel environment instances. Beyond scalable infrastructure, MAI-Gym enables automated synthesis of diverse GUI applications and challenging tasks, including resettable mocked apps and coherent cross-app worlds with execution-validated task verifiers. Using MAI-Gym, we train MAI-Gym-9B, a unified 9B agent for mobile and computer use, achieving state-of-the-art performance among open GUI agents under 10B parameters: 78.4% on MobileWorld and 76.2% on OSWorld-Verified. Importantly, RL in a mocked train-ticketing app transfers to its real-world counterpart, improving task success on a phys- ical phone from 28.8% to 48.1%. We release the framework, synthesis skills, mocked apps, 2,000 verified tasks, and the model checkpoint to support repro- ducible research and further advances in scalable GUI agent training.

cite

@misc{tong2026mai,
  title={MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environments},
  author={Panrong Tong and Quyu Kong and Pengxiang Li and Tianyu Xia and Hanzhang Zhou and Lei Wang and Chenglin Cai and Long Li and Long Chen and Jianan Zhang and Zhengwen Zeng and Xu Zhang and Gongjie Zhang and Yue Wang and Steven Hoi},
  year={2026},
  eprint={2610.20939},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2610.20939}
}