Large language models have achieved significant advancements in complex mathematical reasoning benchmarks, such as MATH. However, their substantial computational requirements present challenges for practical deployment. Model quantization has emerged as an effective strategy to reduce memory usage and computational costs by employing lower precision and bit-width representations. In this study, we systematically evaluate the impact of quantization on mathematical reasoning tasks. We introduce a multidimensional evaluation framework that qualitatively assesses specific capability dimensions and conduct quantitative analyses on the step-by-step outputs of various quantization methods. Our results demonstrate that quantization differentially affects numerical computation and reasoning planning abilities, identifying key areas where quantized models experience performance degradation.

本研究解决了大语言模型在数学推理中面临的高计算需求问题，提出了一种多维评估框架来系统评估量化对数学推理任务的影响。研究发现，量化对数字计算和推理规划能力的影响存在差异，识别了量化模型性能下降的关键领域。

量化与推理的结合：探索大语言模型低比特量化对数学推理的影响