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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 7·5 min read
When Load-Balancing Goes Too Far: Expert Pruning in Over-Dispersed Mixture-of-Experts Models
TL;DR
Expert pruning in Mixture-of-Experts (MoE) models can fail under over-dispersed routing, leading to misleading importance signals. The proposed Minimax Expert Score Allocation (MESA) method improves pruning outcomes by minimizing worst-case performance degradation across tasks.
✦ Why It Matters
Consider implementing MESA for expert pruning in your MoE models to enhance performance across diverse tasks.
Key Takeaways
- Evaluate your MoE model's routing strategy to avoid over-dispersion.
- Implement MESA to improve expert pruning outcomes.
- Monitor task-specific performance metrics during pruning.
- Adjust importance scoring based on worst-case domain performance.
How It Works
MESA iteratively boosts importance scores for experts based on the current worst-performing domain, ensuring that pruning decisions minimize the worst-case degradation rather than average performance.