This approach demonstrates improved performance and reduced inference costs in mixture-of-experts models, suggesting practical applications for large language models.
Key Points
The use of compressed experts can lower active parameters by over 30% while maintaining model performance.
Experiments on Phi-MoE and OLMoE show compressed experts recover over 90% of full expert performance across various tasks.
Fewer activated experts reduce computational costs, indicating a balanced approach to model scaling.
This method provides a practical solution for deploying large models in resource-constrained environments.