M+Adam 优化器改进低精度大语言模型训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-14 04:00

研究人员推出 M+Adam，这是一种新颖的优化方法，旨在提高低精度权重下大语言模型训练的准确性。标准优化器在低精度下可能会遇到困难，导致进展停滞，尤其是在权重幅度较大时。M+Adam 通过结合加法和乘法更新类型来解决这个问题，利用它们互补的优势来确保在各种权重幅度和符号变化下都能取得持续的进展。使用 LLaMA 类型模型和不同精度级别（包括 BF16、FP8 和 FP4）进行的实验表明，M+Adam 持续提高了低精度训练的成果。 AI

影响 M+Adam 通过降低精度要求，可能降低计算成本和硬件需求，从而实现更高效的大模型训练。

排序理由该集群包含一篇详细介绍用于训练机器学习模型的新优化方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xiaoyuan Liang, Sebastian Loeschcke, Mads Toftrup, Anima Anandkumar · 2026-07-14 04:00

M+Adam：通过加性-乘性优化实现低精度训练

arXiv:2607.10611v1 Announce Type: new Abstract: Training with quantized weights can reduce costs but often results in degraded accuracy, especially when optimization is carried out in low precision, without storing high-precision copies. We identify a key failure mode: under low …

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报道来源 [1]

M+Adam：通过加性-乘性优化实现低精度训练

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