新的SOMA算法通过高效的锐度惩罚增强LLM训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员推出了一种新的优化技术——锐度感知正交动量算法（SOMA），旨在提高大型语言模型（LLM）的泛化能力和鲁棒性。SOMA将计算高效的扰动方案与Muon算法相结合，利用陈旧的随机梯度噪声进行对抗性扰动。该方法旨在减轻LLM中传统锐度惩罚的高昂成本。该论文从理论上证明了SOMA的样本复杂度与扰动方向无关，并探讨了其在减少训练后量化造成的质量下降以及防止LLM微调过程中灾难性遗忘方面的有效性。 AI

影响引入了一种更有效的LLM训练方法，有望提高其性能并减少量化降级和灾难性遗忘等问题。

排序理由该集群包含一篇详细介绍LLM优化新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Geetika, Shivani Singh Chauhan, Gauranshi Gupta, Anant Jain, Kaushik Kalyanaraman, Bapi Chatterjee · 2026-10-27 04:00

锐度感知正交动量算法

arXiv:2610.32465v1 Announce Type: new Abstract: Adversarial sharpness penalty per iteration of a base optimizer, known as sharpness-aware minimization (SAM), is an accepted technique to improve generalization in deep learning. Recently, Muon has emerged as a dominant optimization…

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锐度感知正交动量算法

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