Harder Tasks Need More Experts: Dynamic Routing in MoE Models

TL;DR

This paper presents a dynamic expert selection framework for Mixture of Experts models that adjusts the number of active experts based on input difficulty, improving efficiency and performance over fixed routing methods.

Contribution

The paper introduces a novel dynamic expert routing method that activates experts based on confidence, optimizing resource use and enhancing model performance.

Findings

Achieves 0.7% performance improvement over Top-2 routing.

Activates less than 90% of parameters while maintaining accuracy.

Allocates more experts to complex reasoning tasks like BBH.

Abstract

In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adjusting the number of activated experts based on input difficulty. Unlike traditional MoE approaches that rely on fixed Top-K routing, which activates a predetermined number of experts regardless of the input's complexity, our method dynamically selects experts based on the confidence level in expert selection for each input. This allows for a more efficient utilization of computational resources, activating more experts for complex tasks requiring advanced reasoning and fewer for simpler tasks. Through extensive evaluations, our dynamic routing method demonstrates substantial improvements over conventional Top-2 routing across various benchmarks, achieving an average improvement of 0.7% with less than 90% activated parameters. Further analysis shows our model dispatches more experts to tasks requiring complex reasoning skills, like BBH, confirming its ability to dynamically allocate computational resources in alignment with the input's complexity. Our findings also highlight a variation in the number of experts needed across different layers of the transformer model, offering insights into the potential for designing heterogeneous MoE frameworks. The code and models are available at https://github.com/ZhenweiAn/Dynamic_MoE.