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AI Research
arXiv CS.AI·Jun 9·5 min read
Post-Trained MoE Can Skip Half Experts via Self-Distillation
TL;DR
Existing dynamic Mixture-of-Experts (MoE) models require extensive pre-training, limiting their efficiency. The Zero-Expert Self-Distillation Adaptation (ZEDA) framework allows post-trained MoE models to dynamically skip unnecessary experts during inference.
✦ Why It Matters
Engineers can leverage ZEDA to enhance the efficiency of existing MoE models without extensive retraining.
Key Takeaways
- Explore ZEDA for efficient MoE model adaptation.
- Utilize self-distillation to enhance model performance.
- Reduce inference costs by skipping unnecessary experts.
- Achieve faster processing with minimal accuracy trade-off.
How It Works
ZEDA transforms static MoE models into dynamic ones by adding zero-output experts, which do not contribute to computations but help stabilize the model during adaptation. The two-stage self-distillation process involves using the original MoE as a teacher to guide the learning of the adapted model, ensuring that performance remains high while allowing for significant reductions in the number of active experts.