OSGym: Scalable OS Infra for Computer Use Agents
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

TL;DR
OSGym is a scalable, resource-efficient OS infrastructure enabling large-scale computer use agent training with robust error recovery and significant cost reductions.
Contribution
The paper introduces OSGym, a novel OS environment infrastructure with decentralized management, hardware-aware orchestration, and optimized virtualization for scalable agent training.
Findings
Managed over a thousand OS replicas with constrained resources.
Supported parallel trajectory generation at 1420 multi-turn trajectories per minute.
Reduced per-replica cost to 0.2-0.3 USD per day, a 90% reduction.
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 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,…
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