Comparative Analysis of Two Techniques of Advancing MoE

Advancing Mixture-of-Experts (MoE) Optimization: A Comparative Analysis of Hybrid Optimization Techniques vs. Large-Scale MoE Implementation in Moonlight

Abstract

Background & Motivation

Mixture-of-Experts (MoE) models have emerged as a promising approach to scalable and efficient deep learning, particularly in large-scale Natural Language Processing (NLP) tasks. The Moonlight model by Moonshot AI & UCLA represents an applied large-scale MoE implementation, demonstrating the feasibility of MoE models with 3B--16B parameters trained on 5.7 trillion tokens. However, despite Moonlight's efficiency in sparse expert activation and large-scale deployment, its optimization remains static, with limited adaptability, knowledge transfer, and computational efficiency improvements beyond model scaling.

Conversely, the Hybrid Optimization of MoE framework introduces a novel set of optimization strategies, including Dynamic Hierarchical Mixture-of-Experts (DHM), Knowledge Distillation, and Sparse-Dense Fusion, to improve expert selection, training convergence, and inference stability. This paper aims to scientifically analyze the advantages of Hybrid Optimization techniques over Moonlight's large-scale implementation by evaluating efficiency, adaptability, training cost, and model performance.

Methods

• Comparative Theoretical Analysis: Examining how Hybrid MoE Optimization enhances expert selection, reduces computational overhead, and enables knowledge transfer, whereas Moonlight remains a static MoE framework with large-scale training.

• Computational Efficiency Benchmarking: Evaluating training stability, inference latency, and memory consumption in both models.

• Performance Evaluation: Assessing how Hybrid MoE Optimization improves model robustness, generalization, and sample efficiency compared to Moonlight's MoE implementation.

Key Findings

• Hybrid Optimization of MoE significantly reduces training cost and improves computational efficiency through DHM-based adaptive expert selection and Knowledge Distillation, whereas Moonlight relies on static expert routing.

•