How does the auxiliary-loss-free load balancing strategy in DeepSeek-V3 affect expert utilization diversity an

Abstract

Abstract: We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning

Research Question

How does the auxiliary-loss-free load balancing strategy in DeepSeek-V3 affect expert utilization diversity and downstream code generation accuracy on HumanEval compared to traditional auxiliary-loss methods?

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