ZEDA injects zero-output experts and uses two-stage self-distillation to adapt post-trained MoE models into dynamic ones that skip over half the experts, yielding 1.2x inference speedup with small accuracy drops.
Expert threshold routing for autoregressive language modeling with dynamic computation allocation and load balancing.arXiv preprint arXiv:2603.11535,
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Post-Trained MoE Can Skip Half Experts via Self-Distillation
ZEDA injects zero-output experts and uses two-stage self-distillation to adapt post-trained MoE models into dynamic ones that skip over half the experts, yielding 1.2x inference speedup with small accuracy drops.