New Rotation-Based Technique Reduces Weight Outliers for Post-Training Quantization

Global: New Rotation-Based Technique Reduces Weight Outliers for Post-Training Quantization

A team of researchers—Advait Gadhikar, Riccardo Grazzi, and James Hensman—presented a method called OptRot that targets weight outliers in large language models to improve post‑training quantization. The work was first submitted to arXiv on December 30, 2025 and revised on January 12, 2026. By applying data‑free rotations that minimize the element‑wise fourth power of rotated weights, the authors aim to lower quantization error without requiring additional training data. Their approach addresses a key obstacle in deploying efficient, low‑precision models across diverse hardware platforms.

Quantization Challenges in Large Language Models

Quantizing the weights and activations of large language models (LLMs) often encounters difficulties because extreme outlier values can dominate the distribution, leading to substantial accuracy loss when reduced to low‑bit representations. Traditional techniques either rely on data‑dependent calibration or employ generic transformations such as Hadamard rotations, which may not fully address the outlier problem.

Introducing OptRot: Data-Free Rotations

OptRot learns fusible rotation matrices by directly minimizing a proxy for the quantization error. Specifically, the method reduces weight outliers by minimizing the sum of the fourth powers of each rotated weight element, a computationally cheap objective that does not require access to training data. The authors focus on GPTQ as the underlying quantization algorithm, integrating OptRot as a preprocessing step.

Performance Compared to Existing Techniques

Experimental results reported in the abstract indicate that OptRot outperforms both Hadamard rotations and more computationally intensive data‑dependent methods such as SpinQuant and OSTQuant in weight‑only quantization scenarios. In the W4A8 configuration—four‑bit weights with eight‑bit activations—the technique also yields measurable gains in activation quantization quality.

Data-Dependent Extension OptRot+

The paper introduces a variant, OptRot+, which incorporates activation covariance information to refine the rotation matrices further. This data‑dependent extension demonstrates additional performance improvements over the baseline OptRot, particularly when activation statistics are available.

Trade‑Offs in Low‑Bit Settings

When both weights and activations are quantized to four bits (W4A4), the authors observe that OptRot and OptRot+ perform worse than some alternative methods. This outcome highlights a trade‑off between reducing weight outliers and preserving activation fidelity in extremely low‑precision regimes.

Future Directions

The findings suggest that rotation‑based preprocessing can be a valuable tool for post‑training quantization, especially in contexts where data access is limited. Ongoing research may explore hybrid strategies that balance data‑free and data‑dependent components to mitigate the identified trade‑offs.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.