Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers
Foundation models are increasingly being deployed in contexts where understanding the uncertainty of their outputs is critical to ensuring responsible deployment. While Bayesian methods offer a principled approach to uncertainty quantification, their computational overhead renders their use impractical for training or inference at foundation model scale. State-of-the-art models achieve parameter counts in the trillions through carefully engineered sparsity including Mixture-of-Experts (MoE) layers. In this work, we demonstrate calibrated uncertainty at scale by introducing Variational Mixture-of-Experts Routing (VMoER), a structured Bayesian approach for modelling uncertainty in MoE layer...
| Authors | Albus Yizhuo Li, Matthew Wicker |
|---|---|
| Primary category | cs.LG |
| Published | 10 March 2026 |
| Last updated at source | 28 May 2026 |
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