Beyond Retraining-Free MoE Compression: A Cost-Normalized Study of Post-Compression Adjustment

Abstract:Retraining-free MoE compression reduces deployment memory by pruning or merging experts, but often treats the compressed checkpoint as the final artifact. We argue that this view is incomplete: compressed MoE checkpoints are better understood as compressed initializations that benefit from a tiny post-compression adjustment stage. Across two MoE LLM backbones, four pruning/merging methods, three expert-retention ratios, and 28 benchmarks, we compare LM fine-tuning and teacher-based KD under matched small-data budgets and measured GPU costs. Using only 3,000 C4 examples and a single epoch of adjustment, Full FT recovers 37.3% of the original-to-compressed performance gap on average. Moreover, LM fine-tuning is more cost-effective than standard token-level KD, and full-parameter adjustment gives the strongest cost--recovery trade-off among the tested scopes. These results suggest that retraining-free compression should be paired with small post-compression adjustment to recover a substantial portion of the performance lost during compression.

Machine Learning (cs.LG)

arXiv:2609.06076 [cs.LG]

(or arXiv:2609.06076v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.06076 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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