vLLM 推出 vime：简单稳定的 LLM 后训练 RL 框架
做 LLM 后训练的团队终于有了 vLLM 生态内的 RL 框架选择——vime 简单稳定，直接可用，想尝试不同 RL 框架的开发者值得关注。
vLLM 项目宣布推出 vime，一个在 vLLM 生态中用于 LLM 后训练的强化学习框架。vime 基于 slime 的训练设计，并利用 vLLM 推理引擎，提供简单、稳定且高效的 RL 训练方案。该框架旨在与 NeMo RL、OpenRLHF、verl 等共存，为用户提供更多选择。vime 的推出丰富了 vLLM 后训练生态，推动互操作性和创新。
Today we're excited to introduce vime — a simple, stable, and efficient RL framework for LLM post-training in the vLLM ecosystem.
Built on slime's proven training design and powered by vLLM inference, vime brings another strong option to the growing vLLM post-training ecosystem.
Our goal isn't a one-size-fits-all framework. We want users with different needs to find the right vLLM-ecosystem choice for their workflows—whether that's vime, NeMo RL, OpenRLHF, verl, or others.
More choice. More interoperability. More innovation.
Learn more: https://t.co/c3yfEewsWj
#LLM #RLHF #PostTraining #vLLM
