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

• URL: http://arxiv.org/abs/2510.13079v1
• Date: Wed, 15 Oct 2025 01:47:45 GMT
• Title: GatePro: Parameter-Free Expert Selection Optimization for Mixture-of-Experts Models
• Authors: Chen Zheng, Yuhang Cai, Deyi Liu, Jin Ma, Yiyuan Ma, Yuan Yang, Jing Liu, Yutao Zeng, Xun Zhou, Siyuan Qiao,
• Abstract summary: GatePro is a novel parameter-free method that directly promotes expert selection diversity.<n>Our comprehensive evaluation demonstrates GatePro's effectiveness across model scales and benchmarks.<n>This approach can be deployed hot-swappable during any training phase without additional learnable parameters.
• Score: 22.458582284833266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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