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Why Does Post-Training Quantization Work?

Research Efficiency & Systems

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TL;DR - This paper explains why post-training quantization preserves LLM performance despite introducing weight errors at every layer. Pretraining creates error-canceling residual interactions, while LM-head geometry protects the probabilities of highly ranked tokens.

  • Newly introduced layer errors tend to oppose inherited errors, slowing hidden-state discrepancy growth.
  • This counteracting behavior develops during pretraining and is absent in randomly initialized models.
  • LM-head geometry preferentially preserves scores and probabilities for the model’s most confident token predictions.
  • The authors verify both mechanisms across multiple models and quantization settings.

Sources (1)

Why Does Post-Training Quantization Work?

arXiv cs.LG Yuxiang Chen, Michael Beyer, Jun Zhu, Jianfei Chen 2026-09-10 arXiv:2609.11716
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-19 14:15:01.335002 UTC

TL;DR - This paper explains why post-training quantization preserves LLM performance despite introducing weight errors at every layer. Pretraining creates error-canceling residual interactions, while LM-head geometry protects the probabilities of highly ranked tokens.

  • Newly introduced layer errors tend to oppose inherited errors, slowing hidden-state discrepancy growth.
  • This counteracting behavior develops during pretraining and is absent in randomly initialized models.
  • LM-head geometry preferentially preserves scores and probabilities for the model’s most confident token predictions.
  • The authors verify both mechanisms across multiple models and quantization settings.
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