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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 18·5 min read
Routing Divergence Is Not Evidence of Behavioral Influence in Same-Weight MoE Self-Distillation
TL;DR
Routing divergence in Mixture-of-Experts (MoE) models does not indicate behavioral influence during self-distillation, as the routing term's impact is minimal compared to the content term. This finding challenges assumptions about expert routing in model training.
✦ Why It Matters
Evaluate routing effects in your MoE models to ensure accurate interpretations of self-distillation outcomes.
Key Takeaways
- Different experts can be used without changing weights in MoE models.
- Routing divergence does not imply behavioral influence in self-distillation.
- Measure exposure to understand the impact of routing changes.
- Content terms have a stronger effect on model outputs than routing terms.
How It Works
The study uses a blockwise decomposition approach to separate the routing term from the content term in MoE models. By analyzing the contributions of each term, researchers can assess how routing decisions affect model outputs.
This method allows for a clearer understanding of the mechanisms at play during self-distillation.