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arXiv · 2603.22324

DAQ: Delta-Aware Quantization for Post-Training LLM Weight Compression

Xiaoming Yu·Shize Tang·Guanghua Yu·Linchuan Xie·Song Liu·Jianchen Zhu·Feng Li
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Abstract

We introduce Delta-Aware Quantization (DAQ), a data-free post-training quantization framework that preserves the knowledge acquired during post-training. Standard quantization objectives minimize reconstruction error but are agnostic to the base model, allowing quantization noise to disproportionately corrupt the small-magnitude parameter deltas ($\Delta W$) that encode post-training behavior -- an effect we analyze through the lens of quantization as implicit regularization. DAQ replaces reconstruction-based objectives with two delta-aware metrics -- Sign Preservation Rate and Cosine Similarity -- that directly optimize for directional fidelity of $\Delta W$, requiring only the base and post-trained weight matrices. In a pilot FP8 study, DAQ recovers style-specific capabilities lost under standard quantization while maintaining general performance.

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Xiaoming Yu, Shize Tang, Guanghua Yu, Linchuan Xie, Song Liu, Jianchen Zhu, Feng Li. 2026-03-20. DAQ: Delta-Aware Quantization for Post-Training LLM Weight Compression. https://arxiv.org/abs/2603.22324

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