Shared Low-rank Basis Factorization for Data-free Mixture-of-Experts Compression

Abstract:Mixture-of-Experts (MoE) large language models decouple capacity from compute through sparse routing, but their large parameter count creates storage and serving challenges. We analyze three MoE compression families: expert pruning, expert merging, and weight reconstruction, and derive structural error bounds showing that pruning and merging can incur non-vanishing errors tied to routing and expert heterogeneity. In contrast, weight reconstruction avoids these structural costs by preserving expert structure and routing. Motivated by the analysis, we propose Shared Low-rank Basis Factorization (SLBF), a data-free weight reconstruction method that uses rank-$k$ bases shared among experts, enabling richer cross-expert sharing, faster convergence, and lower reconstruction error. A post-hoc gauge fixing removes redundant parameters at no representational cost. Across five MoE architectures spanning 16B to 122B parameters, SLBF consistently outperforms methods from all three compression families.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.09342 [cs.LG]

(or arXiv:2610.09342v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.09342 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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