vLLM V0 to V1: Correctness Before Corrections in RL

Back to Articles vLLM V0 to V1: Correctness Before Corrections in RL Enterprise Article Published May 6, 2026 Upvote - Rafael Pardinas rafapi-snow Follow ServiceNow-AI Ehsan Kamalloo ehsk Follow 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. TL;DR. 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. The reference run used vLLM 0.8.5; the V1 runs used vLLM 0.18.1. Figure 1 shows the final result. The red run is the initial V1 attempt, and the green run is the final V1 run after the fixes described below. Figure 1. Trainer-side metrics for the vLLM V0 reference (blue), the initial vLLM V1 attempt (red), and the final vLLM V1 run after our fixes (green), including the fp32 lm_head. The final V1 run returns close to the V0 trajectory across clip rate, KL, entropy, and reward. Migration Objective vLLM V1 is a substantial rewrite of the V0 engine. Our migration target was therefore deliberately...