DeepSeek Elastic Compute:A Sandbox Infrastructure for Effective Agentic Training

DeepSeek published a technical report on DeepSeek Elastic Compute (DSec), a production sandbox platform for large-scale agentic training and evaluation with large language models, submitted to arXiv on 19 Sep 2026. A single production-scale unit of DSec spans around 160 nodes, serves about 3 million sandboxes per day, supports over 380,000 concurrent sandboxes, and sustains over 5,000 sandbox creations per second. DSec exposes FnCall, container, microVM, and full-VM sandbox backends through a unified SDK, loads image data on demand from the Fire-Flyer File System (3FS), and is co-designed with the reinforcement learning framework to decouple stateful rollout execution from preemptible GPU training and mitigate agent misbehavior such as reward hacking.

Computer Science Distributed, Parallel, and Cluster Computing Submitted on 19 Sep 2026 Title:DeepSeek Elastic Compute DSec : A Sandbox Infrastructure for Effective Agentic Training at Scale View PDF https://arxiv.org/pdf/2609.22978 HTML experimental https://arxiv.org/html/2609.22978v1 Abstract:Large-scale agentic training and evaluation with large language models LLMs rely on isolated, stateful execution environments in which models inspect repositories, invoke tools, execute commands, and interact with task-specific services. These workloads create sandboxes in large bursts, span heterogeneous functionality and isolation requirements, retain state across long interactions, and draw from large image corpora with limited reuse. Supporting them therefore requires an elastic execution platform rather than a single sandbox runtime. This report presents DeepSeek Elastic Compute DSec , a production sandbox platform that exposes FnCall, container, microVM, and full-VM sandbox backends through a unified SDK. DSec coordinates placement and lifecycle management across the cluster, composes environments from independently versioned layers, combines memory sharing, reclamation, and CPU scheduling for high-density execution, and loads image data on demand from Fire-Flyer File System 3FS , a cluster-wide distributed filesystem. DSec is co-designed with the reinforcement learning RL framework, decouples stateful rollout execution from preemptible GPU training, coordinates sandbox lifecycle with training to preserve rollout state while reclaiming idle resources, and mitigates agent misbehavior such as reward hacking. A single production-scale unit of DSec spans around 160 nodes, serving about 3 million sandboxes per day; in production, it supports over 380,000 concurrent sandboxes and sustains over 5,000 sandbox creations per second. Our evaluation and deployment experience show that these mechanisms reduce environment setup and image-distribution overhead, improve memory efficiency, and preserve latency-sensitive performance under high-density overcommit. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .