Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts
Mixture-of-Experts (MoE) has become the dominant architecture for scaling large language models: frontier models routinely decouple total parameters from per-token computation through sparse expert routing. Scaling laws show that under fixed active computation, model quality scales predictably with total parameters, and MoEs realize this by increasing expert count. However, training large MoEs is expensive, as memory requirements and inter-device communication both scale with total parameter count. We propose expert upcycling, a method for progressively expanding MoE capacity by increasing the number of experts during continued pre-training (CPT). Given a trained E-expert model, the upcyc...
| Authors | Chaitanya Dwivedi, Binxuan Huang, Himanshu Gupta et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 April 2026 |
| Last updated at source | 10 May 2026 |
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