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arXiv · 2505.07260

UMoE: Unifying Attention and FFN with Shared Experts

Yuanhang Yang·Chaozheng Wang·Jing Li
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Abstract

Sparse Mixture of Experts (MoE) architectures have emerged as a promising approach for scaling Transformer models. While initial works primarily incorporated MoE into feed-forward network (FFN) layers, recent studies have explored extending the MoE paradigm to attention layers to enhance model performance. However, existing attention-based MoE layers require specialized implementations and demonstrate suboptimal performance compared to their FFN-based counterparts. In this paper, we aim to unify MoE designs in attention and FFN layers by introducing a novel reformulation of the attention mechanism, that reveals an underlying FFN-like structure within attention modules. Our proposed architecture, UMoE, achieves superior performance through attention-based MoE layers while enabling efficient parameter sharing between FFN and attention components.

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Yuanhang Yang, Chaozheng Wang, Jing Li. 2025-05-12. UMoE: Unifying Attention and FFN with Shared Experts. https://arxiv.org/abs/2505.07260

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