Grouter: Decoupling Routing from Representation for Accelerated MoE Training
arXiv:2603.06626v1 Announce Type: new Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneously train expert weights while searching for an optimal routing policy within a vast combinatorial space. This entanglement often leads to sluggish convergence and training instabilities. This paper introduces Grouter, a preemptive routing method that by distilling high-quality structures from fully-trained MoE models and serving as a fixed router for target models. By decoupling structural optimization from weight updates, Grouter significantly accelerates both the speed and quality of model convergence. To ensure the framework's versatility, we also introduce expert folding to adapt Grouter across varying model configurations and expert tuning to rebalance workloads across different data distributions. Furthermore, by leveraging the structural priors provided by preemptive routing, we can impleme
arXiv:2603.06626v1 Announce Type: new Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneously train expert weights while searching for an optimal routing policy within a vast combinatorial space. This entanglement often leads to sluggish convergence and training instabilities. This paper introduces Grouter, a preemptive routing method that by distilling high-quality structures from fully-trained MoE models and serving as a fixed router for target models. By decoupling structural optimization from weight updates, Grouter significantly accelerates both the speed and quality of model convergence. To ensure the framework's versatility, we also introduce expert folding to adapt Grouter across varying model configurations and expert tuning to rebalance workloads across different data distributions. Furthermore, by leveraging the structural priors provided by preemptive routing, we can implement targeted optimizations to further enhance training throughput. Experiments demonstrate that Grouter achieves superior performance and efficiency which boosts pre-training data utilization by 4.28x and achieves up to 33.5% throughput acceleration, establishing preemptive routing as a fundamental paradigm for scalable MoE training.
Executive Summary
This study introduces Grouter, a preemptive routing method that decouples structural optimization from weight updates in Mixture-of-Experts (MoE) training, significantly accelerating model convergence. By leveraging structural priors, Grouter boosts pre-training data utilization by 4.28x and achieves up to 33.5% throughput acceleration. The authors also introduce expert folding and tuning to adapt Grouter across varying model configurations and data distributions. Experiments demonstrate Grouter's superiority in performance and efficiency, establishing preemptive routing as a fundamental paradigm for scalable MoE training. This breakthrough has significant implications for the field of deep learning, particularly in large-scale MoE models.
Key Points
- ▸ Grouter decouples structural optimization from weight updates in MoE training
- ▸ Preemptive routing significantly accelerates model convergence
- ▸ Expert folding and tuning adapt Grouter across varying model configurations and data distributions
Merits
Strength in decoupling structural optimization from weight updates
Grouter's decoupling approach enables faster model convergence and more efficient training, addressing a major limitation in traditional MoE training.
Demerits
Limitation in model assumption
Grouter assumes a fixed routing structure, which may not be adaptable to complex or dynamic data distributions.
Expert Commentary
The introduction of Grouter marks a significant advancement in the field of deep learning, particularly in MoE models. By decoupling structural optimization from weight updates, Grouter addresses a major limitation in traditional MoE training, enabling faster model convergence and more efficient training. The authors' use of expert folding and tuning to adapt Grouter across varying model configurations and data distributions further enhances the framework's versatility. While Grouter assumes a fixed routing structure, which may limit its adaptability to complex or dynamic data distributions, this breakthrough has significant implications for the field of deep learning. Future research should focus on developing more adaptable and dynamic routing structures to further enhance Grouter's capabilities.
Recommendations
- ✓ Further research should be conducted to develop more adaptable and dynamic routing structures to enhance Grouter's capabilities.
- ✓ The development of Grouter highlights the need for further investment in research and development of efficient training methods for large-scale neural networks.