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About this paper
Jakub Krajewski, Marcin Chochowski, Daniel Korzekwa
The paper provides the first comprehensive empirical evaluation of fine-grained Mixture-of-Experts (MoE) architectures at scales exceeding 50 billion parameters, demonstrating consistent performance improvements over standard MoE variants in pretraining loss and downstream task accuracy, often with reduced active parameter usage.
Key insights
- The advantages of fine-grained MoE become more pronounced with longer training horizons, as the router gradually learns to effectively utilize multiple experts over time.
- Standard load balancing techniques effectively maintain uniform expert load distribution even with the increased number of experts in fine-grained configurations.
- For multi-expert routing (k > 1), applying softmax after Top-k selection is crucial for performance, differing from Top-1 models where softmax precedes Top-k.
Summary by alphaXiv.
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