Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs

• URL: http://arxiv.org/abs/2608.04048v1
• Date: Tue, 04 Aug 2026 08:32:05 GMT
• Title: Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs
• Abstract summary: We introduce Recurrent Residual Quantization (RRQ), a post-training quantization framework.<n>RRQ represents weights as a low-bit quantized base together with a sequence of quantized residual corrections.<n>In our Qwen3-8B setup, the full all-RTN 2-/4-/6-/8-bit package is constructed in 1,293 seconds, 3.3 times faster than the measured MatGPTQ construction.
• Score: 9.68624792431081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Serving large language models (LLMs) under diverse deployment constraints requires flexible trade-offs between accuracy, memory footprint, and throughput. However, conventional quantization methods typically require a separate checkpoint for each target bit-width. We introduce Recurrent Residual Quantization (RRQ), a post-training quantization (PTQ) framework that represents weights as a low-bit quantized base together with a sequence of quantized residual corrections, enabling multiple effective precisions from a single checkpoint. Starting from a 2-bit model obtained via post-training quantization (PTQ) or round-to-nearest (RTN), RRQ progressively adds lightweight 2-bit residuals generated via RTN to construct 4-, 6-, and 8-bit representations. The method is calibration-free and avoids joint multi-bit optimization. In our Qwen3-8B setup, the full all-RTN 2-/4-/6-/8-bit package is constructed in 1,293 seconds, 3.3 times faster than the measured MatGPTQ construction. Experiments on six recent LLMs show competitive accuracy at 6 and 8 bits, with model-dependent behavior at 4 bits. The code will be made publicly available upon publication.

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