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

Ahead of the upcoming open-source release of GLM-5.3, we’re releasing slime v0.3.2 🚀 Highlights: - Fully aligned GLM-5 training in Megatron with SGLang rollout - Tons of community-contributed bug fixes and optimizations - Continued refactoring and codebase cleanup, including the new slime/observability module - CUDA 13 support Getting slime ready for GLM-5.3 👀

slime now adds --release-train, pushing the inference system during agentic RL training to a new limit. In colocated RL training, we want SGLang to use as much room as possible for inference-side optimizations such as HiCache, instead of being constrained by offloaded Megatron training processes. --release-train makes this possible by releasing the Megatron training process during rollout and reloading it for each training round. This gives SGLang more configuration headroom in colocated RL workloads. PR:

🚀 slime v0.3.0 is out! This release is a major step toward agent-first RL. We turned slime’s existing multi-turn / agentic capabilities into a more coherent foundation: - slime/agent with reusable sandbox-agent components - OpenAI / Anthropic-compatible adapters - black-box coding-agent RL example - variable global batch-size training - fully async training as a first-class path - lower host-memory usage for more flexible rollout-inference setups - PPO refactor with actor-critic colocation - delta weight sync, FlashQLA for Qwen GDN, --save-hf, and more CI coverage slime is moving closer to a practical open-source framework for large-scale agentic RL. Release note: