---
id: 20260407-T0-12
title: "LiME：高效多模态多任务学习的轻量级专家混合模型"
title_en: "LiME: Lightweight MoE for Efficient Multimodal Learning"
url: https://ai.daily.yangsir.net/daily/20260407-T0-12
issue_date: 2026-04-07
publish_date: 2026-04-06T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2604.02338
---

# LiME：高效多模态多任务学习的轻量级专家混合模型

arXiv论文提出LiME（Lightweight Mixture of Experts），一种针对多模态多任务学习的高效专家混合模型。现有MoE-PEFT方法需为每个专家单独适配器，导致参数量随专家数量线性增长。LiME通过创新架构解决了这一问题，显著减少了可训练参数数量，同时保持了多任务适应能力，为资源受限场景下的多模态模型部署提供了新思路。

## English Version

**LiME: Lightweight MoE for Efficient Multimodal Learning**

arXiv paper introduces LiME, a lightweight Mixture of Experts for efficient multimodal multi-task learning. Existing MoE-PEFT methods require separate adapters per expert, causing linear parameter scaling. LiME's novel architecture reduces trainable parameters while maintaining multi-task adaptability, enabling deployment in resource-constrained scenarios.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2604.02338)

**详情页**：https://ai.daily.yangsir.net/daily/20260407-T0-12

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