Unverified50% confidenceFactExact time
When a model's sampling parameters (such as temperature and top_p) cause the output format to deviate from the training distribution, vLLM's parser may fail to match boundaries correctly, resulting in truncated parameters or format errors.
1
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50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
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Related Claims
UnverifiedThe numerical inconsistency problem is especially pronounced in heterogeneous architectures that use vLLM or TensorRT-LLM for rollout generation and PyTorch/DeepSpeed for training.70% similarUnverifiedWhen an LLM scores as a judge, even with temperature set to 0, the same conversation trajectory and judge model run five times still produces a different verdict once, and this non-determinism is unacceptable for a test suite that wants to be integrated into CI.68% similarUnverifiedLLM judge results are affected by factors such as temperature, prompt wording, and model version updates, causing the same set of test cases to potentially produce different conclusions when run at different times.68% similarUnverifiedIn a 2025 analysis, Thinking Machines Lab named the vLLM temperature=0 nondeterminism problem "lack of batch invariance."68% similarVerifiedThe non-determinism of LLMs stems from sampling strategies; the Temperature parameter controls the randomness of the output distribution, while Top-p and Top-k further constrain the range of candidate tokens.68% similar
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