这篇论文用两个通用语料就能做好MoE剪枝，在Qwen和DeepSeek模型上准确率更高，尤其适合需要极限压缩的场景。
该论文提出Generic TB-Coverage方法，仅使用WikiText2和C4两个通用语料进行校准。通过分别评估每个语料库上的专家效用并执行固定预算覆盖规则来构建剪枝掩码。在Qwen1.5-MoE-A2.7B和DeepSeek-MoE-16B-Base上，以25%、50%、75%的保留预算测试，六个零样本基准的平均准确率均优于随机剪枝、REAP和ExpertSparsity，同时WikiText2和C4的困惑度退化更小。在激进剪枝（25%和50%保留）下增益最为显著。
Generic Expert Coverage for Pruning SparseMixture-of-Experts Language Models
Sparsely activated Mixture-of-Experts (MoE) language models contain substantial structured redundancy among routed experts, but pruning them without downstream calibration data remains challenging. Existing expert-pruning methods typically rely on a single aggregated importance score, which can bias the retained set toward experts favored by dominant calibration patterns. We propose \textbf{Generic TB-Coverage}, a coverage-aware expert pruning method that uses only generic text corpora (WikiText2 and C4) for calibration. Instead of collapsing expert utility into one score, our method profiles per-expert utility separately on each corpus and enforces a fixed-budget coverage rule that preserves high-utility experts from each corpus before constructing the final pruning mask. Across Qwen1.5-MoE-A2.7B and DeepSeek-MoE-16B-Base at 25\%, 50\%, and 75\% retention budgets, our method improves average accuracy on six common zero-shot benchmarks over random pruning, REAP, and ExpertSparsity, while also reducing perplexity degradation on WikiText2 and C4. The gains are largest under aggressive pruning (25\% and 50\% retain), suggesting that preserving cross-corpus expert coverage is an effective generic-data prior for MoE pruning. Our improvements hold with fixed pruning budgets and no downstream calibration data.