alphaXiv(@askalphaxiv):“Why Does Post-Training Quantization Work?” This paper shows that pretrained LLMs are inherently robust to quantization because layers actively counteract accumulated quantization errors, while high-dimensional LM-head geometry preferentially preserves top-token predictions. This lets 4-bit models stay close to full precision despite substantial hidden-state perturbations.

2026.09.13 03:59

“Why Does Post-Training Quantization Work?” This paper shows that pretrained LLMs are inherently robust to quantization because layers actively counteract accumulated quantization errors, while high-dimensional LM-head geometry preferentially preserves top-token predictions. This lets 4-bit models stay close to full precision despite substantial hidden-state perturbations.

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