---
id: 20260716-T0-10
title: "CARE-LoRA：压缩激活重建技术，将微调内存占用降低40%"
title_en: "CARE-LoRA: Compressed Activation Reconstruction Cuts Fine-Tuning Memory by 40%"
url: https://ai.daily.yangsir.net/daily/20260716-T0-10
issue_date: 2026-07-16
publish_date: 2026-07-15T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2607.11940
---

# CARE-LoRA：压缩激活重建技术，将微调内存占用降低40%

arXiv新论文提出CARE-LoRA方法，通过压缩并选择性重建前向传播中的激活值，大幅降低LoRA微调时的内存占用。实验表明，该方法在保持模型精度的同时，可将内存需求降低约40%，使得在更小显存的GPU上微调大模型成为可能。

## English Version

**CARE-LoRA: Compressed Activation Reconstruction Cuts Fine-Tuning Memory by 40%**

A new arXiv paper introduces CARE-LoRA, which compresses and selectively reconstructs activations during forward passes, significantly reducing memory usage in LoRA fine-tuning. Experiments show it cuts memory requirements by about 40% while maintaining model accuracy, enabling fine-tuning on GPUs with less VRAM.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2607.11940)

**详情页**：https://ai.daily.yangsir.net/daily/20260716-T0-10

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