Predictive model of PTQ quantization degradation and contributing factors

Determine whether a predictive model of post-training quantization-induced degradation in large language models can be developed and identify the additional training or model factors that contribute to quantization degradation beyond those analyzed in this study.

Background

The paper conducts a large-scale analysis of post-training quantization (PTQ) robustness across multiple open-source LLM training trajectories and controlled experiments. It finds that learning rate scheduling and certain training interventions such as weight averaging correlate with PTQ-induced degradation, while gradient norm magnitudes do not. Weight decay shows some correlation with improved robustness.

Despite these observations, the authors report erratic behavior in some training runs and state that it is still unclear whether a reliable predictive model for quantization degradation can be formulated or which other factors may be responsible for the variability.

References

As a result, it remains unclear whether a predictive model of quantization degradation is within reach, or what additional factors may be at play.

— Training Dynamics Impact Post-Training Quantization Robustness  (2510.06213 - Catalan-Tatjer et al., 7 Oct 2025) in Section 6 (Discussion)

Two questions stay open: does the quality curve, whose direction holds on three sizes without a scaling law, reach the 70B class, and which untested lever (calibration composition, learned column scales, low-rank compensation) closes the reasoning-concentrated MMLU deficit?

— Unfolding the Leech Lattice: Fused Multi-Shell Decoding and VRAM Layouts for 2-Bit LLM Weights  (2609.02652 - Malandrino, 2 Sep 2026) in Conclusion, final paragraph; see also Section 7