aiqbench analysis
This research addresses a key bottleneck in Large Language Model deployment by improving post-training quantization accuracy through quantile-robust scaling and learned channel optimization. By mitigating errors caused by outlier-dominated channels, the proposed method achieves significant reductions in quantization error without requiring full model retraining. These findings offer a practical pathway for reducing serving costs while maintaining performance in resource-constrained environments.
Trainable Smooth-Rotation Transforms with Learned Channel Scales for LLM Quantization
arXiv:2606.09927v1 Announce Type: new Abstract: Post-training quantization (PTQ) is one of the most practical ways to reduce the serving cost of Large Language Models (LLMs), but activation quantization remains difficult because outlier-dominated channels lead to large quantization errors. This paper investigates whether part of this degradation is caused by over-migration in scaling-based equivalent transformations. We introduce a quantile-robust scaling policy for SmoothRot-style transforms by replacing max-based activation statistics with high quantiles, and we complement it with constrained gradient-based optimization of channel scales. On LLaMA-3.2-1B under W4A4 quantization, quantile-only policy search improves selected-layer error by 11.1% over the SmoothRot baseline, joint (alpha, q) search improves it by 12%, and training reaches 18.5%. Replaying the best selected-layer policy on all decoder-block down-projection layers reduces the corresponding full-layer mean error from 97.51 to 78.08 (19.9%). The results show that robust migration control and lightweight scale learning provide consistent gains over max-based fixed policies while preserving the equivalent-transform framework.
Abstract:Post-training quantization (PTQ) is one of the most practical ways to reduce the serving cost of Large Language Models (LLMs), but activation quantization remains difficult because outlier-dominated channels lead to large quantization errors. This paper investigates whether part of this degradation is caused by over-migration in scaling-based equivalent transformations. We introduce a quantile-robust scaling policy for SmoothRot-style transforms by replacing max-based activation statistics with high quantiles, and we complement it with constrained gradient-based optimization of channel scales. On LLaMA-3.2-1B under W4A4 quantization, quantile-only policy search improves selected-layer error by 11.1% over the SmoothRot baseline, joint (alpha, q) search improves it by 12%, and training reaches 18.5%. Replaying the best selected-layer policy on all decoder-block down-projection layers reduces the corresponding full-layer mean error from 97.51 to 78.08 (19.9%). The results show that robust migration control and lightweight scale learning provide consistent gains over max-based fixed policies while preserving the equivalent-transform framework.
6 pages, 8 figures, 3 tables. Accepted to IEEE INES 2026 conference proceedings
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2606.09927 [cs.LG]
(or arXiv:2606.09927v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.09927 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 262 words