JARQ: Joint Alternating Refinement for Quantization

Xinyu Wang; Sicheng Lyu; Xiao-Wen Chang

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arXiv · 2609.38599

JARQ: Joint Alternating Refinement for Quantization

Xinyu Wang·Sicheng Lyu·Xiao-Wen Chang

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Abstract

Group-wise post-training quantizers for large language models round weights onto a grid that is not refit to the resulting integer codes. We show that this leaves accuracy on the table: the best grid depends on the codes, input correlations couple the errors of different groups, and useful code changes often involve many codes at once. We propose JARQ , a plug-in refinement that starts from any group-wise quantizer and alternates a joint least-squares fit of all group scales with bounded Babai proposals that move many codes of a group together on the current grid. The problem is a bilinear box-constrained mixed-integer least-squares problem; the solver is backpropagation-free, does not increase the layer-wise objective under exact scale solves, and keeps the host's bit width, groups, zero points, and inference cost. Across Llama-2, Llama-3, and Qwen models with RTN, GPTQ, OmniQuant, and AWQ hosts, JARQ lowers perplexity in 90 of 96 comparisons, cuts three-bit RTN perplexity by up to 36%, raises mean multiple-choice accuracy in 23 of 24 configurations, and improves QEP, QuaRot, and OJBKQ outputs, at under a minute per 7B block.

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BibTeX RIS

Xinyu Wang, Sicheng Lyu, Xiao-Wen Chang. 2026-10-02. JARQ: Joint Alternating Refinement for Quantization. https://arxiv.org/abs/2609.38599

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