OpenAI's Navier-Stokes effort used ~10,000 concurrent agents and ~130B output tokens. It made us rethink the scale ahead for RL infrastructure. slime v0.4.0 is a first step toward greater scale, with four architectural changes: • Focus: synchronous training for algorithmic exploration and correctness; fully async training for throughput at scale. • Distributed rollout: spread orchestration across cluster CPUs, with independent generation pools and event loops. • Persistent storage: introduce straw, our new storage middleware for rollout and training data, providing durable queues and shared tensor storage. • Independent recovery: restart Megatron while healthy SGLang engines stay running, preserving accepted rollout work. More on the architecture below 👇 1/5