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English(EN) Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling

AdaLeZO 通过自适应层采样加速 LLM 微调

研究人员开发了 AdaLeZO,一个旨在提高大型语言模型微调的零阶 (ZO) 优化效率的新框架。该方法通过动态地将计算资源分配给模型中最敏感的层,解决了 ZO 通常伴随的收敛速度慢和方差大的问题。AdaLeZO 可作为即插即用模块,在不增加内存使用量的情况下,将现有 ZO 优化器的速度提高高达 3.0 倍。 AI

排序理由 这是一篇关于 LLM 新优化框架的研究论文。

在 Hugging Face Daily Papers 阅读 →

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AdaLeZO 通过自适应层采样加速 LLM 微调

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  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    通过自适应层级采样实现通用赋能的零阶优化

    Zeroth-Order optimization presents a promising memory-efficient paradigm for fine-tuning Large Language Models by relying solely on forward passes. However, its practical adoption is severely constrained by slow wall-clock convergence and high estimation variance. In this work, w…