DeepSeek Elastic Compute (DSec)
DeepSeek Elastic Compute (DSec) is a production sandbox infrastructure designed to support large-scale agentic training and evaluation for large language models. The platform provides a unified SDK that manages diverse sandbox backends, including function calls, containers, and virtual machines, while optimizing resource usage through memory sharing and on-demand data loading. DSec is co-designed with reinforcement learning frameworks to decouple stateful execution from GPU training, ensuring efficient resource reclamation and mitigation of agent misbehavior.
- ▪DSec exposes FnCall, container, microVM, and full-VM sandbox backends through a unified software development kit.
- ▪The platform utilizes the Fire-Flyer File System (3FS) to load image data on demand from a cluster-wide distributed filesystem.
- ▪DSec coordinates sandbox lifecycle management to preserve rollout state while reclaiming idle resources during preemptible GPU training.
- ▪The infrastructure is specifically designed to handle workloads that create sandboxes in large bursts and retain state across long interactions.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.22978 |
| Publication time | Sat, 26 Sep 2026 18:22:41 +0000 |
| Retrieval time | 2026-09-26T20:30:40.244Z |
| Last seen | 2026-09-26T20:30:40.244Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | SVMUu8Eszba0 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2609.22978 (cs) [Submitted on 19 Sep 2026] Title:DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale Authors:Jialiang Huang, Hongxuan Tang, Jingchang Chen, Yuxuan Liu, Yixiao Chen, Yuan Cheng, Yi Tao, Jingli Zhou, Yupeng Chen, Haoyu Chen, Jiarui Wang, Shengkai Lin, Chuqi Zhang, Bryan Lee Teng, Lian Guo, Zhe Fu, Wenjun Gao, Yisong Wang, Liang Zhao, Zehao Wang, Ziwei Xie, Yongqiang Guo, Peixin Cong, Ziyi Gao, Shuiping Yu, Hanwei Xu, Zuofan Wu, Zhizhou Ren, Yuyang Zhou, Bowei Zhang, Zhihuan Huang, Qihao Zhu, Lei Wang, Tianle Lin, Han Yu, Jiewen Hu, Dejian Yang, Shuo Yang, Shanghao Lu, Shaoyuan Chen, Junjie Qiu, Zhangli Sha, Yinmin Zhong, Yongtong Wu, Shiyu Wang, Wei Liu,…
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