The potential trade-offs between the robustness and redundancy provided by top-K expert selection and the efficiency gains from a more deterministic top-1 expert selection lie in the balance between model performance and computational efficiency.
Robustness and Redundancy (Top-K): Top-K expert selection offers robustness by allowing multiple experts to contribute to the final prediction, providing a form of redundancy that can enhance model accuracy and resilience to noise or uncertainty in the data. However, this redundancy comes at the cost of increased computational overhead due to activating multiple experts per token.
Efficiency (Top-1): On the other hand, a more deterministic top-1 expert selection optimizes computational efficiency by activating only the most relevant expert for each token. This approach reduces the number of experts involved in the inference process, leading to lower FLOPs and memory usage. However, the trade-off is a potential decrease in model robustness and the risk of losing valuable information that could be captured by multiple experts.
Balancing these trade-offs involves considering the specific requirements of the task at hand. In scenarios where accuracy and robustness are paramount, top-K expert selection may be preferred despite the increased computational cost. Conversely, for applications where efficiency is critical and minor accuracy trade-offs are acceptable, a more deterministic top-1 expert selection could be the preferred choice.