Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts

Lean Wang, Huazuo Gao, Chenggang Zhao, Xu Sun, Damai Dai

Keywords: mixture of experts, load balancing, auxiliary-loss-free

TL;DR: We propose Loss-Free Balancing, a novel MoE load balancing method that dynamically adjusts expert biases based on its recent load without relying on auxiliary losses, thereby avoiding interference gradients and achieving improved model performance.

Abstract: For Mixture-of-Experts (MoE) models, an unbalanced expert load will lead to routing collapse or increased computational overhead. Existing methods commonly employ an auxiliary loss to encourage load balance, but a large auxiliary loss will introduce non-negligible interference gradients into training and thus impair the model performance. In order to control load balance while not producing undesired gradients during training, we propose **Loss-Free Balancing**, a new load balancing strategy that operates without auxiliary losses. To be specific, before the top-K routing decision, Loss-Free Balancing will first apply an expert-wise bias to the routing scores of each expert. By dynamically updating the bias of each expert according to its recent load, Loss-Free Balancing can consistently maintain a balanced distribution of expert load. In addition, since Loss-Free Balancing does not produce any interference gradients, it also elevates the upper bound of model performance gained from MoE training. We validate the performance of Loss-Free Balancing on MoE models with up to 3B parameters trained on up to 200B tokens. Experimental results show that Loss-Free Balancing achieves both better performance and better load balance compared with traditional auxiliary-loss-controlled load balancing strategies.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 3529