这个框架解决了MoE模型在内存受限设备上部署的痛点，做模型压缩和边缘部署的团队可以直接参考其方法，比传统剪枝效果更好且训练更快。
该研究提出了首个系统化框架，将混合专家模型（MoE）转换为标准全稠密架构。通过专家评分、选择、分组并拼接成稠密前馈网络，再通过知识蒸馏从MoE教师模型精炼。在Qwen3-30B-A3B上评估了7种评分、5种分组和2种幅度缩放方法，共350种配置。发现评分方法影响最大，其提出的多样性感知评分在多个模型上优于先前方法。在参数匹配控制下，MoE转稠密比稠密到稠密剪枝平均下游准确率提升6.3个百分点，训练速度快1.6倍。
Pruning and Distilling Mixture-of-Experts into Dense Language Models
Mixture-of-Experts (MoE) is now the dominant architecture for frontier language models, yet it requires all expert parameters to be loaded in memory, making it less preferable for memory-constrained deployment. Existing compression methods reduce the number of experts but the output remains an MoE model with the same fundamental limitation. We present the first systematic framework for converting a trained MoE into a standard fully dense architecture: experts are scored, selected, and grouped, then concatenated into a dense FFN and refined by knowledge distillation from the MoE teacher. We evaluate 7 scoring, 5 grouping, and 2 magnitude scaling methods across a range of selected expert counts on Qwen3-30B-A3B, yielding 350 configurations. We find that the choice of scoring method is the most impactful, with our novel diversity-aware scoring consistently outperforming prior methods on Qwen3-30B-A3B, DeepSeek-V2-Lite, and GPT-OSS-20B. Under a controlled comparison at matched parameter count, MoE-to-dense outperforms dense-to-dense pruning by +6.3 pp in average downstream accuracy after ~4B-token distillation at 1.6x faster training wall-clock speed.