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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.11672 |
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| _version_ | 1866910095732899840 |
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| author | Qin, Zengyi Chen, Jinyuan Man, Yunze Cao, Shengcao Pang, Ziqi Wang, Zhuoyuan Fang, Han Zhu, Ling Xie, Zixin Wei, Zibu Ran, Tianshu Geng, Haoran Pan, Ray Sun, Qizhen Bright, Zachary Cai, Yuyang Yang, Chongye Zhao, Jiace Liu, Tianrui Cao, Han Zhou, Yeyang Wang, Rui Wang, Song Ren, Xiang Zhang, Bo Ban, Yutong Abbeel, Pieter Anthony, Brian |
| author_facet | Qin, Zengyi Chen, Jinyuan Man, Yunze Cao, Shengcao Pang, Ziqi Wang, Zhuoyuan Fang, Han Zhu, Ling Xie, Zixin Wei, Zibu Ran, Tianshu Geng, Haoran Pan, Ray Sun, Qizhen Bright, Zachary Cai, Yuyang Yang, Chongye Zhao, Jiace Liu, Tianrui Cao, Han Zhou, Yeyang Wang, Rui Wang, Song Ren, Xiang Zhang, Bo Ban, Yutong Abbeel, Pieter Anthony, Brian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11672 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OSGym: Scalable OS Infra for Computer Use Agents Qin, Zengyi Chen, Jinyuan Man, Yunze Cao, Shengcao Pang, Ziqi Wang, Zhuoyuan Fang, Han Zhu, Ling Xie, Zixin Wei, Zibu Ran, Tianshu Geng, Haoran Pan, Ray Sun, Qizhen Bright, Zachary Cai, Yuyang Yang, Chongye Zhao, Jiace Liu, Tianrui Cao, Han Zhou, Yeyang Wang, Rui Wang, Song Ren, Xiang Zhang, Bo Ban, Yutong Abbeel, Pieter Anthony, Brian Distributed, Parallel, and Cluster Computing 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. |
| title | OSGym: Scalable OS Infra for Computer Use Agents |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2511.11672 |