vLLM V0 to V1: Correctness Before Corrections in RL

vLLM V0 to V1: Correctness Before Corrections in RL vLLM V0 to V1: Correctness Before Corrections in RL Enterprise Article Published May 6, 2026 Upvote 13 Rafael Pardinas rafapi-snow ServiceNow-AI Ehsan Kamalloo ehsk ServiceNow-AI PipelineRL uses vLLM as the inference engine for rollout generation. The inference engine samples tokens and returns token logprobs; the trainer uses those logprobs to compute policy ratios, KL, clip rate, entropy, and reward.

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• vLLM V0 to V1: Correctness Before Corrections in RL Enterprise Article Published May 6, 2026 Upvote 13 Rafael Pardinas rafapi-snow ServiceNow-AI Ehsan Kamalloo ehsk ServiceNow-AI PipelineRL uses vLLM as the inference engine for rollout generation.
• The inference engine samples tokens and returns token logprobs; the trainer uses those logprobs to compute policy ratios, KL, clip rate, entropy, and reward.
• Any discrepancy in how those logprobs are computed can change the training dynamics.
• This is the train-inference mismatch we needed to eliminate during the vLLM V0 to V1 migration.
• vLLM V1 matched our vLLM V0 reference after we fixed four things: processed rollout logprobs, V1-specific runtime defaults, the inflight weight-update path, and the fp32 lm_head used for the final projection.
• We fixed the backend behavior before changing the RL objective.