vLLM V1 Skews RL Results: Why Inference Correctness Beats Speed

This week, the ServiceNow AI team disclosed test results: in a Reinforcement Learning (RL — a method where AI self-improves through trial and error and reward signals) pipeline, vLLM V1 and V0 exhibit systematic output differences for the same prompt. This directly causes the reward model to assign different scores, skewing the training trajectory.

What this is

vLLM is currently one of the most mainstream open-source LLM inference frameworks (the underlying software responsible for efficiently running trained models and serving them externally), maintained by the UC Berkeley team. Late last year, it initiated a major version refactor from V0 to V1. The core change: using a more aggressive scheduling strategy to achieve higher throughput. The problem: V1 applies approximate calculations to certain operators for speed. In standard dialogue scenarios, the difference is negligible. But in RL pipelines—where models need to precisely reproduce their historical outputs to calculate rewards—tiny numerical deviations are amplified round by round. ServiceNow's tests show that the same checkpoint produces divergent token sequences under V0 and V1, making the final policy evaluation results incomparable.

Industry view

Community reactions are split. Frontline deployment teams consider this migration growing pain; vLLM officially acknowledged the issue, marked it as a priority on their fix roadmap, and is expected to provide a strict numerical consistency mode in subsequent versions. However, the other faction's voice demands our attention: multiple RL researchers point out that this issue is not unique to vLLM. All inference engines pursuing extreme performance (TensorRT-LLM, TGI, etc.) are making similar speed-for-accuracy trade-offs; most users simply adopt the "fast" version before ever reaching RL scenarios. We must be vigilant: if the industry defaults to "speed over accuracy," more training pipelines will unknowingly introduce systematic noise in the future.

Impact on regular people

For enterprise IT: If your company is building an internal model training platform, you must add a numerical regression test before upgrading the inference framework. Otherwise, the alignment effectiveness of RLHF (Reinforcement Learning from Human Feedback, the key training method for ChatGPT) may silently degrade.

For individual careers: The barrier to entry for AI engineering roles is shifting from "knowing how to tune APIs" to "understanding the underlying stack." Those who can identify and troubleshoot such precision issues will have significantly higher bargaining power than those who only know how to use the latest frameworks.

For the consumer market: There will be no perceptible impact in the short term—end users see the trained product, not the inference framework. But if precision issues at the framework layer continue to be ignored, we may see a cluster of "alignment quality regression" models launching next year.