ConMoE: MoE Compression as Expert Selection and Functional Assignment
Sparse Mixture-of-Experts (MoE) models scale model capacity through sparse activation, but storing their full parameters remains costly in GPU memory. Existing expert compression methods address this problem through pruning, merging, or substitution, typically coupling expert pool construction with the assignment of routed workloads. In this work, we formulate expert compression as two decisions--constructing an expert pool and assigning the original experts' workloads to that pool--and show that assignment can be optimized after the pool is fixed. Based on this formulation, we introduce ConMoE, which constructs a compact pool using Anchor Coverage and optimizes assignment through a route...
| Authors | Yilun Yao, Jiaming Pan, Elsie Dai et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 May 2026 |
| Last updated at source | 12 October 2026 |
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