Path-Constrained Mixture-of-Experts
arXiv:2603.18297v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling by activating only a subset of parameters for each input. However, conventional MoE routing selects each layer's experts independently, creating N^L possible expert paths -- for N experts across L layers. This far exceeds typical training set sizes, leading to statistical inefficiency as the model may not learn meaningful structure over such a vast path space. To constrain it, we propose \pathmoe, which shares router parameters across consecutive layers. Experiments on 0.9B and 16B parameter models demonstrate consistent improvements on perplexity and downstream tasks over independent routing, while eliminating the need for auxiliary load balancing losses. Analysis reveals that tokens following the same path naturally cluster by linguistic function, with \pathmoe{} producing more concentrated groups, better cross-layer consistency, and greater robustness to routing pe
arXiv:2603.18297v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling by activating only a subset of parameters for each input. However, conventional MoE routing selects each layer's experts independently, creating N^L possible expert paths -- for N experts across L layers. This far exceeds typical training set sizes, leading to statistical inefficiency as the model may not learn meaningful structure over such a vast path space. To constrain it, we propose \pathmoe, which shares router parameters across consecutive layers. Experiments on 0.9B and 16B parameter models demonstrate consistent improvements on perplexity and downstream tasks over independent routing, while eliminating the need for auxiliary load balancing losses. Analysis reveals that tokens following the same path naturally cluster by linguistic function, with \pathmoe{} producing more concentrated groups, better cross-layer consistency, and greater robustness to routing perturbations. These results offer a new perspective for understanding MoE architectures through the lens of expert paths.
Executive Summary
The article introduces Path-Constrained Mixture-of-Experts (MoE), a novel architecture that constrains expert path selection in MoE models by sharing router parameters across consecutive layers. This approach eliminates the need for auxiliary load balancing losses and demonstrates consistent improvements over independent routing on perplexity and downstream tasks. Analysis reveals that tokens following the same path naturally cluster by linguistic function, with Path-Constrained MoE producing more concentrated groups, better cross-layer consistency, and greater robustness to routing perturbations. The proposed method offers a new perspective on understanding MoE architectures through the lens of expert paths, with potential applications in natural language processing and deep learning.
Key Points
- ▸ Path-Constrained MoE shares router parameters across consecutive layers to constrain expert path selection.
- ▸ The approach eliminates the need for auxiliary load balancing losses.
- ▸ Experiments demonstrate consistent improvements over independent routing on perplexity and downstream tasks.
- ▸ Analysis reveals that tokens following the same path naturally cluster by linguistic function.
Merits
Improved Efficiency
Path-Constrained MoE reduces the vast path space of conventional MoE routing, leading to improved efficiency and scalability.
Enhanced Performance
The proposed method demonstrates consistent improvements over independent routing on perplexity and downstream tasks, indicating improved model performance.
Demerits
Increased Complexity
Path-Constrained MoE introduces additional parameters and complexity, which may require increased computational resources and training time.
Limited Generalizability
The proposed method may not generalize to other domains or applications beyond natural language processing and deep learning.
Expert Commentary
The article makes a significant contribution to the field of deep learning and natural language processing by introducing a novel architecture that constrains expert path selection in MoE models. The proposed method offers a new perspective on understanding MoE models through the lens of expert paths, which can lead to improved efficiency, performance, and scalability. However, the increased complexity and limited generalizability of the proposed method are potential drawbacks that require further investigation. Overall, the article is well-written and clearly presents the proposed method and its benefits, making it a valuable addition to the field of deep learning and natural language processing.
Recommendations
- ✓ Future research should investigate the application of Path-Constrained MoE to other domains and applications beyond natural language processing and deep learning.
- ✓ The proposed method should be further explored and refined to address the potential drawbacks of increased complexity and limited generalizability.
