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arXiv · 2610.09342

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

Tianxiao Cao·Jiahe Shao·Yuning Qiu·Kyohei Atarashi·Hisashi Kashima·Qibin Zhao
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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.

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Tianxiao Cao, Jiahe Shao, Yuning Qiu, Kyohei Atarashi, Hisashi Kashima, Qibin Zhao. 2026-10-07. Shared Low-rank Basis Factorization for Data-free Mixture-of-Experts Compression. https://arxiv.org/abs/2610.09342

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