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Why Does Post-Training Quantization Work?

arxiv.org/abs/2609.11716

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Updated 44 min ago · first seen 11 Sept 2026

paper_01M294FPB1VK57J4F3Q9S0P4T7

Published
11 Sept 2026
T1 · 44 min ago
arXiv
2609.11716
T1 · 44 min ago
Category
cs.LG
T1 · 44 min ago

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ValueValid from → toStatusSourceConfidenceExtractor
Post-training quantization compresses large language models (LLMs) by storing their weights at reduced precision, and each quantized weight introduces an error into the hidden states. Naively, these errors should accumulate with depth and corrupt next-token prediction; randomly initialized models accumulate these discrepancies rapidly, whereas quantized pretrained models accumulate much less hidden-state error and largely maintain downstream task performance, even though they were never trained with quantization noise. This raises the question we address: why does post-training quantization work? Comparing full-precision and quantized forward passes, we identify two mechanisms that characterize pretrained quantization robustness. First, the error a layer newly introduces tends to oppose the error it inherits from the layer's input. The two cancel partially such that the discrepancy between full-precision and quantized passes grows slowly. This counteracting residual interaction develops during pretraining. Our quantitative analysis identifies it as a major factor slowing hidden-error growth. Second, LM-head geometry preferentially preserves the scores and probabilities of high-ranked tokens, which typically represent the model's most confident predictions. Together, these mechanisms explain why quantization error that passes through numerous layers can still produce only small output changes, and we verify the findings across models and quantization settings. → currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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