English(EN) CodeQuant2: Enhanced Outlier Mitigation and Adaptive Clustering for Low-Precision LRMs

CodeQuant2框架通过改进量化来增强低精度LRM

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 05:00

研究人员推出了一种新颖的量化框架CodeQuant2，旨在提高低精度大型推理模型（LRM）的性能。该方法通过旋转和缩放来缓解激活值异常值，并通过聚类来表示非均匀权重分布，从而解决了推理任务中的挑战。CodeQuant2还根据层量化难度自适应调整码本大小，并优化校准样本选择以更好地匹配解码统计数据，在推理基准测试中取得了显著的准确性和速度提升。 AI

影响该框架通过低精度量化提高准确性和速度，有可能实现更高效的大型推理模型的部署。

排序理由该集群包含一篇详细介绍用于提高LLM性能的新技术框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Xingyu Liu, Xiangyang Yin, Tianhua Xia, Haiyu Wang, Sai Qian Zhang · 2026-11-09 05:00

CodeQuant2：低精度LRM的增强异常值缓解和自适应聚类

arXiv:2611.09868v1 Announce Type: new Abstract: Large reasoning models (LRMs) improve task performance by generating longer intermediate reasoning traces, but this substantially increases inference costs and makes low-precision quantization increasingly important. However, aggres…

本文如何被排名

报道来源 [1]

CodeQuant2：低精度LRM的增强异常值缓解和自适应聚类

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