OSGym: Scalable OS Infra for Computer Use Agents
Authors:
Zengyi Qin,
Jinyuan Chen,
Yunze Man,
Shengcao Cao,
Ziqi Pang,
Zhuoyuan Wang,
Han Fang,
Ling Zhu,
Zixin Xie,
Zibu Wei,
Tianshu Ran,
Haoran Geng,
Ray Pan,
Qizhen Sun,
Zachary Bright,
Yuyang Cai,
Chongye Yang,
Jiace Zhao,
Tianrui Liu,
Han Cao,
Yeyang Zhou,
Rui Wang,
Song Wang,
Xiang Ren,
Bo Zhang
, et al. (3 additional authors not shown)
Abstract:
Training computer use agents requires full-featured OS sandboxes with GUI environments, which consume substantial hardware resources as the number of sandboxes scales. Stochastic errors arising from diverse software execution within these sandboxes further demand robust infrastructure design and reliable error recovery. We present OSGym, a scalable OS environment infrastructure for computer use ag…
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Training computer use agents requires full-featured OS sandboxes with GUI environments, which consume substantial hardware resources as the number of sandboxes scales. Stochastic errors arising from diverse software execution within these sandboxes further demand robust infrastructure design and reliable error recovery. We present OSGym, a scalable OS environment infrastructure for computer use agents, built around these key optimization strategies: (1) Decentralized OS state management, which isolates failures to individual replicas and significantly enhances overall system reliability; (2) Hardware-aware OS replica orchestration, which addresses CPU-bounded scaling bottlenecks and substantially reduces compute overhead; (3) KVM virtualization with copy-on-write disk management, which shares a common bootable disk across VM instances and provisions only instance-specific modifications, reducing physical disk consumption by 88% and increasing disk provisioning speed by 37 times; and (4) Robust container pool with multi-layer fault recovery. Together, these optimizations yield strong scalability and resource efficiency: OSGym manages over a thousand OS replicas under constrained resources, supports parallel trajectory generation at 1420 multi-turn trajectories per minute, and reduces per-replica cost to 0.2-0.3 USD per day, a 90% reduction over standard deployment. Our experiments validate OSGym across end-to-end pipelines for data collection and training for computer use agents. We believe OSGym establishes a new foundation for scalable, general-purpose computer use agent research.
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Submitted 1 April, 2026; v1 submitted 11 November, 2025;
originally announced November 2025.