AI BriefingPath-Constrained Mixture-of-Experts
Key point
We propose PathMoE, which constrains the expert paths that tokens traverse to improve the efficiency and performance of MoE models.
Details
Existing Sparse Mixture-of-Experts (MoE) architectures route tokens independently at each layer. However, research findings show that actual tokens tend to concentrate on specific paths depending on their linguistic function, revealing a statistical inefficiency in which the majority of possible paths go unused.
To address this, this paper proposes the PathMoE architecture. PathMoE deliberately constrains the effective path space that tokens traverse by sharing Router parameters across consecutive layer blocks.
Experimental results show that PathMoE provides the following benefits:
- Enhanced path clustering: Forms naturally occurring path structures more intensively
- Consistency and robustness: Increases path consistency across layers and secures robustness against routing perturbations
- Performance improvement: Improves Perplexity and downstream task performance on 0.9B and 16B parameter models
- Efficiency: Achieves performance gains without a separate auxiliary loss
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