Rethinking Routing Exploration in Modern Mixture-of-Experts
Mixture-of-experts (Mixture-of-Experts AI) models have become an important approach for scaling model parameters. Modern Mixture-of-Experts AI architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts. The former increases expressiveness by enabling more expert combinations at a fixed activation ratio, and the latter introduces e
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Mixture-of-experts (Mixture-of-Experts AI) models have become an important approach for scaling model parameters. Modern Mixture-of-Experts AI architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts. The former increases expressiveness by enabling more expert combinations at a fixed activation ratio, and the latter introduces e
This paper highlights a critical challenge in modern Mixture-of-Experts (MoE) models, specifically the trade-off between fine-grained expert segmentation and shared experts. Understanding this 'exploration dilemma' is crucial for ML engineers designing and optimizing large-scale models, as it directly impacts model expressiveness and efficiency.