ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression 事件

ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression arXiv:2605.29350v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models reduce per-token computation but still require storing and serving all experts, making deployment memory-intensive. Existing post-training compression methods mainly shrink this cost by pruning experts or merging their weights. We formulate post-training MoE compression as expert-pool consolidation: retaining a smaller set of p