Unverified50% confidenceFactExact time
Expert pruning typically targets mixture-of-experts (MoE) models, removing redundant or unimportant experts to compress model size.
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50%
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Long-term
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11/3/2026
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VerifiedMixture of Experts (MoE) models dynamically select a small number of experts to activate for each input token through a gating mechanism, thereby reducing the computational cost per inference while maintaining model capability.73% similarUnverifiedThe fine-tuning approach uses expert data to adjust model parameters so that outputs more closely match the expert's style.70% similarUnverifiedMoE (Mixture of Experts) achieves sparse activation by splitting a large FFN into N smaller FFN experts and using a routing network to decide which experts to activate each time.68% similarUnverifiedMixtral系列采用稀疏混合专家架构（Sparse Mixture of Experts, MoE），在激活参数受限的前提下扩展模型容量65% similarUnverifiedTraditional compression evaluation mainly measures the retained performance of compressed models on benchmark tasks and compares it with the original model, reducing model capability to a single scalar score.64% similar
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