Why Does Post-Training Quantization Work? — Paper

Abstract

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.

Authors 4

Yuxiang Chen, Michael Beyer, Jun Zhu, Jianfei Chen

Specification

Official page: arxiv.org/abs/2609.11716
Source:arXiv (Atom API + RSS)T1observed 44 min agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 44 min agohigh
arXiv id: 2609.11716
Source:arXiv (Atom API + RSS)T1observed 44 min agohigh
Categories: cs.LG, cs.CL
Source:arXiv (Atom API + RSS)T1observed 44 min agohigh
PDF: arxiv.org/pdf/2609.11716
Source:arXiv (Atom API + RSS)T1observed 44 min agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 44 min agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 44 min agohigh

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9

Source tiers

T19

Freshest observation

44 min ago

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Authors: Yuxiang Chen, Michael Beyer, Jun Zhu

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Claim history · Official page

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Claim history for Official page
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2609.11716	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
Property changedPaperWhy Does Post-Training Quantization Work?
Why Does Post-Training Quantization Work?: arxiv announce type changed from new to cross
Arxiv announce typenew→crossarxiv
rss.arxiv.org
11 Sept 2026
New paperPaperWhy Does Post-Training Quantization Work?
New paper: Why Does Post-Training Quantization Work?
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CL	feed	T1· Official	44 min ago	1
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	44 min ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.