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.
vLLM V1 is a substantial rewrite of the V0 engine. Our migration target was therefore deliberately narrow:
Those metrics came from a GSPO training run, the objective used for this experiment. The same class of mismatch can surface in PPO, GRPO, or any online RL system that treats rollout-side logprobs as part of the optimization target.
The initial V1 run showed the problem clearly. The trainer-side logprobs and reward moved away from the V0 reference early in training.
The same pattern appears in the trainer metrics. Clip rate is the easiest signal to read in the initial comparison.
We initially suspected the third category too early. The useful diagnosis came from treating the first two as backend behavior problems and ruling them out first.
The first issue was semantic. vLLM V1 returns logprobs from the raw model outputs by default, before logits post-processing such as temperature scaling, penalties, and top-k/top-p filtering. PipelineRL expected logprobs from the processed distribution used by the sampler.
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