Enhancing Mixture-of-Experts Specialization via Cluster-Aware Upcycling
Sparse Upcycling provides an efficient way to initialize a Mixture-of-Experts (MoE) model from pretrained dense weights instead of training from scratch. However, since all experts start from identical weights and the router is randomly initialized, the model suffers from expert symmetry and limited early specialization. We propose Cluster-aware Upcycling, a strategy that incorporates semantic structure into MoE initialization. Our method first partitions the dense model's input activations into semantic clusters. Each expert is then initialized using the subspace representations of its corresponding cluster via truncated SVD, while setting the router's initial weights to the cluster cent...
| Authors | Sanghyeok Chu, Pyunghwan Ahn, Gwangmo Song et al. |
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| Primary category | cs.CV |
| Published | 15 April 2026 |
| Last updated at source | 17 April 2026 |
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