---
id: 20260421-T0-04
title: "Aletheia实现跨架构LoRA高效微调"
title_en: "Aletheia: Efficient LoRA Fine-Tuning Across Architectures"
url: https://ai.daily.yangsir.net/daily/20260421-T0-04
issue_date: 2026-04-21
publish_date: 2026-04-20T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2604.15351
---

# Aletheia实现跨架构LoRA高效微调

Aletheia研究提出梯度引导的层选择方法，优化LoRA微调效率。该技术能智能选择30%的关键层进行微调，训练速度提升3倍，内存占用减少50%。团队测试了7种主流模型架构，效果均优于标准LoRA。这项创新大幅降低了企业定制大模型的成本门槛。

## English Version

**Aletheia: Efficient LoRA Fine-Tuning Across Architectures**

Aletheia introduces gradient-guided layer selection for optimizing LoRA fine-tuning. The technology intelligently fine-tunes just 30% of key layers, tripling training speed while halving memory usage. Tests on 7 mainstream model architectures all outperformed standard LoRA. This innovation significantly lowers cost barriers for enterprise model customization.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2604.15351)

**详情页**：https://ai.daily.yangsir.net/daily/20260421-T0-04

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