---
id: 20260521-T0-13
title: "HELLoRA：专家模型参数高效微调新方案"
title_en: "HELLoRA: New Efficient Fine-Tuning for Mixture-of-Experts Models"
url: https://ai.daily.yangsir.net/daily/20260521-T0-13
issue_date: 2026-05-21
publish_date: 2026-05-20T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.18795
---

# HELLoRA：专家模型参数高效微调新方案

研究人员提出HELLoRA方法，针对MoE模型的稀疏激活特性进行层级化低秩适配。该方法在保持MoE计算效率的同时，显著降低了微调参数量（减少40%），并在多个基准测试中提升了模型性能。相比传统LoRA，HELLoRA专门优化了专家模型的权重分配，使其在专业领域任务中表现更好。该方案为MoE模型的高效定制提供了新思路，论文已在arXiv发表。

## English Version

**HELLoRA: New Efficient Fine-Tuning for Mixture-of-Experts Models**

Researchers proposed HELLoRA, a layer-level low-rank adaptation method specifically designed for Mixture-of-Experts (MoE) models. It reduces fine-tuning parameters by 40% while maintaining MoE's computational efficiency, and improves performance on multiple benchmarks. Unlike traditional LoRA, HELLoRA optimizes expert weight allocation, enhancing performance on specialized tasks. The paper is published on arXiv.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.18795)

**详情页**：https://ai.daily.yangsir.net/daily/20260521-T0-13

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