English(EN) LazyFlash: Forward-Dense, Backward-Sparse Attention for Accelerated Long-Context Fine-Tuning

LazyFlash 通过新颖的稀疏注意力方法加速大语言模型微调

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员开发了 LazyFlash，这是一种新颖的注意力机制，旨在加速大语言模型（LLMs）在长上下文场景下的微调。LazyFlash 采用前向密集、后向稀疏的方法，确保训练后的模型与 vLLM 等现有推理系统兼容。通过识别并跳过在反向传播过程中对参数更新影响最小的梯度计算，LazyFlash 实现了显著的加速。实验表明，注意力反向传播速度最多可提高 5.7 倍，端到端训练速度可提高 1.7 倍，同时保持下游性能。 AI

影响该方法可以显著降低对长序列大语言模型进行微调所需的计算成本和时间，从而使先进模型更加易于获取。

排序理由介绍大语言模型微调新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Keqi Deng, Ruchao Fan, Jinyu Li · 2026-10-26 04:00

LazyFlash：用于加速长上下文微调的前向密集、后向稀疏注意力

arXiv:2610.31911v1 Announce Type: new Abstract: As large language models (LLMs) process increasingly long sequences, attention becomes a major computational bottleneck. Sparse attention mostly targets inference, yet many methods still train densely or cannot support sparse traini…

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LazyFlash：用于加速长上下文微调的前向密集、后向稀疏注意力

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