This reframing of DSec as a Software Machine reveals new possibilities for distributed computing that go beyond traditional RL approaches. Developers and infrastructure managers can explore how this architecture might solve previously intractable problems by coordinating massive agent populations, potentially offering a competitive alternative to existing distributed systems.
The reveal of DSec provides insights into how DeepSeek optimizes its massive model training infrastructure, which could influence industry approaches to resource allocation. For developers and researchers working with large models, understanding this distributed architecture could inform their own training strategies and resource planning.
DeepSeek 这次放出了训练智能体用的沙箱 DSec，还写了论文讲细节，做 Agent 开发的可以翻翻看。
DeepSeek公开了自家跑智能体训练的沙箱底座，一天能开300万个沙箱，做Agent训练的可以看看这套架构怎么搭。
