---
id: 20260509-T0-09
title: "EMO 架构预训练实现专家混合模型的自发模块化"
title_en: "EMO Pretraining Achieves Emergent Modularity in Mixture of Experts"
url: https://ai.daily.yangsir.net/daily/20260509-T0-09
issue_date: 2026-05-09
publish_date: 2026-05-08T16:03:50.000Z
category: research
source_name: "Hugging Face Blog"
source_url: https://huggingface.co/blog/allenai/emo
---

# EMO 架构预训练实现专家混合模型的自发模块化

最新研究展示了 EMO 架构在混合专家模型预训练中的表现。研究发现，通过特定的预训练策略，MoE 模型可以自发地形成模块化的专家分工，而无需人工干预路由设计。这为后续构建结构更清晰、可解释性更强的大规模稀疏模型提供了新方向。

## English Version

**EMO Pretraining Achieves Emergent Modularity in Mixture of Experts**

New research demonstrates EMO, a pretraining approach for Mixture of Experts (MoE) models. The method induces emergent modularity, allowing experts to naturally specialize without manual routing constraints. This provides a new path for building highly interpretable and efficient sparse models.

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**来源**：[Hugging Face Blog](https://huggingface.co/blog/allenai/emo)

**详情页**：https://ai.daily.yangsir.net/daily/20260509-T0-09

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