Higher-Order Pruning of Experts (HOPE) Keeps MoE Model Expert Pairs Intact

Amazon researchers publishing on Hugging Face have introduced HOPE (Higher-Order Pruning of Experts), a technique for pruning Mixture-of-Experts (MoE) models that preserves co-dependent expert pairs rather than pruning experts independently. Standard expert pruning can inadvertently destroy learned inter-expert coordination, degrading model performance more than the parameter count reduction would suggest. HOPE addresses this by identifying and protecting expert partnerships during the pruning process, yielding better performance-to-size tradeoffs for compressed MoE models. For ML engineers working on model compression and efficient inference of large MoE architectures, this technique offers a practical improvement over naive pruning strategies. The release on Hugging Face Blog suggests accompanying code or model artifacts are available for experimentation.