vLLM V0 到 V1 的演進：在強化學習（RL）中「正確性重於修正」的實踐
Original: vLLM V0 to V1: Correctness Before Corrections in RL
This blog post published by the ServiceNow AI team delves into the major transition of the open-source large language model inference…
ServiceNow AI 發表專文探討 vLLM V0 到 V1 的架構演進。文章指出，在進行大語言模型（LLM）的強化學習（RL）訓練時，底層推理引擎（如 vLLM）的精確度與穩定性至關重要。過去在 V0 版本中，微小的推理偏差或不確定性常導致 RL 訓練難以收斂，迫使研究員進行無謂的演算法修正；而 vLLM V1 透過重構底層，實現了「正確性優先」的設計，大幅提升了 RL 訓練的效率與可預測性。
This blog post published by the ServiceNow AI team delves into the major transition of the open-source large language model inference engine vLLM from V0 to V1, focusing on how this evolution addresses core pain points in reinforcement learning (RL, e.g., RLHF/PPO/GRPO) training.
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