新的Ladder Side Tuning方法大幅降低了LLM微调的内存需求

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员推出了一种名为Ladder Side Tuning（LST）的参数高效微调方法，与QLoRA相比，它显著降低了内存使用量。LST通过引入一个轻量级的侧网络，将峰值内存需求降低了50%，同时在各种自然语言理解和数学任务中保持了具有竞争力的准确性。这种效率使得在单个12GB消费级GPU上微调7B参数模型成为可能，而无需梯度检查点。该研究还提出了LST的深度扩展变体xLadder，通过交叉连接增强了有效模型深度，从而在没有额外内存开销的情况下实现更深层次的推理。 AI

影响使得在消费级硬件上微调大型模型成为可能，可能使先进的LLM定制民主化。

排序理由详细介绍了一种用于大型语言模型参数高效微调的新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Estelle Zheng, Nathan Cerisara, S\'ebastien Warichet, Emmanuel Helbert, Christophe Cerisara · 2026-08-27 04:00

Ladder Up, Memory Down: Low-Cost Fine-Tuning With Side Nets

arXiv:2512.14237v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) is often limited by the memory available on commodity GPUs. Parameter-efficient fine-tuning (PEFT) methods such as QLoRA reduce the number of trainable parameters, yet still incur high me…

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Ladder Up, Memory Down: Low-Cost Fine-Tuning With Side Nets

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