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Mixture of Experts: Router Training Challenges & Solutions

Why naively training a Mixture-of-Experts router causes expert under-training, and the two concrete fixes (noise injection + top-K masking, token capacity limits) that solve it.

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▶📚 Prerequisites(1)

🎓 Learning objectives

  • •Explain why a naively-trained MoE router causes a rich-get-richer expert under-training problem
  • •Describe the noise-injection + top-K-masking fix for router training
  • •Describe the token-capacity-limiting fix for uneven expert token exposure
  • •Explain why MoE achieves faster inference despite having more total parameters

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📂 Subtopics

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Dense Transformer Architecture Recap: Every Parameter Active for Every Token

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Mixture of Experts: Sparse Activation, Smaller "Expert" Feed-Forward Networks

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The Router: How the Model Decides Which Experts to Activate

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MoE Trade-offs: Router Training Challenges and When to Use MoE

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