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GatePro: Parameter-Free Expert Selection Optimization for Mixture-of-Experts Models

Chen Zheng, Yuhang Cai, Deyi Liu, Jin Ma, Yiyuan Ma, Yuan Yang, Jing Liu, Yutao Zeng, Xun Zhou, Siyuan Qiao

TL;DR

GatePro introduces a parameter-free, hot-swappable mechanism to boost diversity among experts in Mixture-of-Experts models by enforcing localized competition between functionally similar experts. It computes a gating-similarity matrix from gating weights, identifies the most similar expert pairs, and applies token-specific competition with a small logit penalty to suppress redundant co-activations, thereby increasing selection entropy and distinctiveness without adding parameters. Across multiple model scales and training phases, GatePro yields consistent gains in reasoning and knowledge tasks, accelerates expert activation, and reduces redundancy, with pronounced effects in deeper layers. The method demonstrates practical applicability, scalability with larger expert pools, and persistent improvements even when GatePro is later disabled, making it a robust enhancement for MoE effectiveness in real-world settings.

Abstract

Modern large language models leverage Mixture-of-Experts (MoE) architectures for efficient scaling, but face a critical challenge: functionally similar experts are often selected simultaneously, creating redundant computation and limiting effective model capacity. Existing auxiliary balance loss methods improve token distribution but fail to address the underlying expert diversity problem. We introduce GatePro, a novel parameter-free method that directly promotes expert selection diversity. GatePro identifies the most similar expert pairs and introduces localized competition mechanisms, preventing redundant expert co-activation while maintaining natural expert specialization. Our comprehensive evaluation demonstrates GatePro's effectiveness across model scales and benchmarks. Analysis demonstrates GatePro's ability to achieve enhanced expert diversity, where experts develop more distinct and complementary capabilities, avoiding functional redundancy. This approach can be deployed hot-swappable during any training phase without additional learnable parameters, offering a practical solution for improving MoE effectiveness.

GatePro: Parameter-Free Expert Selection Optimization for Mixture-of-Experts Models

TL;DR

GatePro introduces a parameter-free, hot-swappable mechanism to boost diversity among experts in Mixture-of-Experts models by enforcing localized competition between functionally similar experts. It computes a gating-similarity matrix from gating weights, identifies the most similar expert pairs, and applies token-specific competition with a small logit penalty to suppress redundant co-activations, thereby increasing selection entropy and distinctiveness without adding parameters. Across multiple model scales and training phases, GatePro yields consistent gains in reasoning and knowledge tasks, accelerates expert activation, and reduces redundancy, with pronounced effects in deeper layers. The method demonstrates practical applicability, scalability with larger expert pools, and persistent improvements even when GatePro is later disabled, making it a robust enhancement for MoE effectiveness in real-world settings.

Abstract

Modern large language models leverage Mixture-of-Experts (MoE) architectures for efficient scaling, but face a critical challenge: functionally similar experts are often selected simultaneously, creating redundant computation and limiting effective model capacity. Existing auxiliary balance loss methods improve token distribution but fail to address the underlying expert diversity problem. We introduce GatePro, a novel parameter-free method that directly promotes expert selection diversity. GatePro identifies the most similar expert pairs and introduces localized competition mechanisms, preventing redundant expert co-activation while maintaining natural expert specialization. Our comprehensive evaluation demonstrates GatePro's effectiveness across model scales and benchmarks. Analysis demonstrates GatePro's ability to achieve enhanced expert diversity, where experts develop more distinct and complementary capabilities, avoiding functional redundancy. This approach can be deployed hot-swappable during any training phase without additional learnable parameters, offering a practical solution for improving MoE effectiveness.
Paper Structure (18 sections, 10 equations, 7 figures, 3 tables, 1 algorithm)

This paper contains 18 sections, 10 equations, 7 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Sparse MoE and routing.
  4. MoE in Open-Source Models.
  5. Approach
  6. Preliminaries: Mixture-of-Experts Layer
  7. GatePro Approach
  8. Experiments
  9. Experimental Setup
  10. Main Results
  11. GatePro Performance Analysis on MoE CT Stage
  12. Comparision with OLMoE
  13. Expert Utilization Analysis
  14. Expert Gating Similarity Analysis
  15. Conclusion
  16. ...and 3 more sections

Figures (7)

  • Figure 1: The GatePro Approach.
  • Figure 2: Performance comparsion on Seed-MoE-0.7B/7B pretrain stage.
  • Figure 3: Performance Comparsion on Seed-MoE-1.3B/13B pretrain stage.
  • Figure 4: Zero token count progression across different layers during training with 128 experts. The figure shows comparison between different configurations across shallow and deep layers: MoE w/o balance loss (green), GatePro w/o balance loss (purple), MoE (orange), and GatePro (blue).
  • Figure 5: Expert gating similarity analysis for Seed-MoE with 128 experts. Metrics at Layer 8 and Layer 16. Each row shows four metrics: average cosine similarity, average angle, and spectral entropy.
  • ...and 2 more figures