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Nora optimizer achieves efficiency, stability, and speed for large-scale LLM training

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have introduced Nora, a novel matrix-based optimizer designed for efficient and stable training of large language models. Nora aims to unify efficiency, stability, and speed, addressing limitations of existing methods like Muon and RMNP. The optimizer stabilizes weight norms and angular velocities, approximates structured preconditioning, and achieves a computational complexity of O(mn), with a simple two-line implementation. AI

影响 Introduces a new optimization technique that could accelerate large-scale LLM training and improve stability.

排序理由 The cluster contains an arXiv preprint detailing a new optimization method for LLM training.

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Nora optimizer achieves efficiency, stability, and speed for large-scale LLM training

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jinghui Yuan, Jiaxuan Zou, Shuo Wang, Yong Liu, Feiping Nie ·

    Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer

    arXiv:2605.03769v1 Announce Type: new Abstract: Matrix-based optimizers have demonstrated immense potential in training Large Language Models (LLMs), however, designing an ideal optimizer remains a formidable challenge. A superior optimizer must satisfy three core desiderata: eff…

  2. arXiv cs.LG TIER_1 English(EN) · Feiping Nie ·

    Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer

    Matrix-based optimizers have demonstrated immense potential in training Large Language Models (LLMs), however, designing an ideal optimizer remains a formidable challenge. A superior optimizer must satisfy three core desiderata: efficiency, achieving Muon-like preconditioning to …

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