pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training

• URL: http://arxiv.org/abs/2602.22592v1
• Date: Thu, 26 Feb 2026 03:51:58 GMT
• Title: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training
• Authors: Wenzheng Zhang, Bingzheng Liu, Yang Hu, Xiaoying Bai, Wentao Zhang, Bin Cui,
• Abstract summary: pQuant is a method that decouples parameters by splitting linear layers into two specialized branches.<n>We show pQuant achieves state-of-the-art performance in extremely low-bit quantization.
• Score: 24.05577787968274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantization-Aware Training from scratch has emerged as a promising approach for building efficient large language models (LLMs) with extremely low-bit weights (sub 2-bit), which can offer substantial advantages for edge deployment. However, existing methods still fail to achieve satisfactory accuracy and scalability. In this work, we identify a parameter democratization effect as a key bottleneck: the sensitivity of all parameters becomes homogenized, severely limiting expressivity. To address this, we propose pQuant, a method that decouples parameters by splitting linear layers into two specialized branches: a dominant 1-bit branch for efficient computation and a compact high-precision branch dedicated to preserving the most sensitive parameters. Through tailored feature scaling, we explicitly guide the model to allocate sensitive parameters to the high-precision branch. Furthermore, we extend this branch into multiple, sparsely-activated experts, enabling efficient capacity scaling. Extensive experiments indicate our pQuant achieves state-of-the-art performance in extremely low-bit quantization.

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