Training stochasticity as a source of quantization-error counteraction

Determine whether training stochasticity produces the counteraction between accumulated block-input hidden error and newly introduced block-update error observed in pretrained Transformer language models.

Background

The paper identifies a counteraction mechanism in which the quantization-induced error entering a Transformer block tends to be opposed by the error introduced by that block. This negative interaction develops during pretraining and substantially slows hidden-error growth relative to randomly initialized models. The authors report that the mechanism is associated primarily with the quantized block’s response to its erroneous input, but they do not establish why training produces this behavior.

The unresolved question is whether stochasticity during training is a causal source of this counteraction. Resolving it would connect the empirical robustness mechanism to theories of implicit bias in stochastic gradient descent.

References

Two questions remain open: whether training stochasticity produces counteraction, as suggested by implicit-bias analyses of SGD~\citep{pmlr-v119-smith20a}, and whether these findings can improve post-training quantization.

— Why Does Post-Training Quantization Work?  (2609.11716 - Chen et al., 10 Sep 2026) in Section 5.4, “Limitations” (Section \ref{sec:scope-limitations})