Research23h ago
EMO Pretraining Drives Emergent Modularity In MoE
The EMO framework uses a specialized pretraining objective to force Mixture-of-Experts models to develop distinct, functional modules. Researchers found that this approach prevents expert collapse and improves specialization across tasks. This method allows developers to prune or swap specific modules without retraining the entire network. It offers a concrete path toward more efficient, modular LLM architectures.