English(EN) Lower Bounds for Smooth Nonconvex Zeroth-Order Optimization: The Price of Determinism under Low Curvature Trace

确定性优化 vs. 随机优化：LLM 微调的效率权衡

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

本文探讨了在高维光滑非凸函数优化中，确定性方法与随机性方法在效率上的对比，特别是在语言模型微调的背景下。研究表明，在处理低曲率迹时，确定性方法的效率显著低于随机方法，需要更多的函数评估。研究结果表明，随机方法在微调等任务中的有效性与其利用这些确定性方法无法匹敌的低曲率条件的能力有关。 AI

影响这项研究突出了优化大型模型的计算权衡，可能影响未来LLM微调的算法设计。

排序理由该条目是发表在arXiv上的学术论文，详细介绍了优化方法的理论研究。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Yuxing Peng, Zhiqing Tang, Weijia Jia · 2026-10-21 04:00

光滑非凸零阶优化的下界：低曲率迹下的确定性代价

arXiv:2610.27550v1 Announce Type: new Abstract: Low curvature trace, an effective rank condition, is a common explanation for why randomized zeroth-order methods scale to high-dimensional tasks such as language model fine-tuning. Can deterministic methods achieve the same savings…