VQV Signal
Greedy decoding in large language models is not precision-invariant, producing different outputs when using BF16 versus FP16 precision on the same hardware. Evaluations across six models and three benchmarks show 49-100% of prompts yield divergent results.
Greedy decoding in large language models is not precision-invariant, producing different outputs when using BF16 versus FP16 precision on the same hardware. Evaluations across six models and three benchmarks show 49-100% of prompts yield divergent results.
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Greedy decoding from large language models is commonly treated as deterministic. We show it is not precision-invariant: the same model, prompt, and decoding algorithm produce different outputs in BF16 versus FP16 on identical hardware. Across our evaluations of six models (1.1B-7B parameters, four families;...
This finding challenges the assumption that greedy decoding is deterministic and highlights the impact of numerical precision on LLM inference consistency. Understanding this divergence is crucial for reproducibility and reliability in deploying LLMs.
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