The vLLM team has launched vime, a new open-source reinforcement learning post-training framework that plugs directly into the vLLM ecosystem. The core idea is straightforward: take the training architecture from slime -- a framework already proven in production on frontier models -- and replace its inference backend with vLLM. The result is a single, unified pipeline for RL fine-tuning that inherits the best of both projects.

Why This Combination Matters

RL post-training (also called RLHF or RLVR) is the stage after pre-training where a model is fine-tuned using reward signals to improve reasoning, instruction-following, or tool use. It is computationally intensive because it alternates between two phases: rollout (the model generates responses, scored by a reward function) and training (the model's weights are updated based on those scores). Running both phases efficiently, on the same cluster, without the two sides drifting apart numerically, is the hard engineering problem.

slime is the RL framework behind GLM-5.1, GLM-5, GLM-4.7, GLM-4.6, and GLM-4.5 -- a lineage of frontier models from Tsinghua's THUDM lab. It connects Megatron with SGLang and passes Megatron arguments through directly, so upstream training and serving optimizations remain available without adding another wrapper layer. The catch: slime uses SGLang as its rollout engine, not vLLM. For teams already invested in the vLLM ecosystem, that's a friction point.

vime connects slime's training stack with vLLM rollouts to provide a simple, stable, and efficient RL post-training pipeline. It is available now, open-sourced under Apache 2.0.