By routing input tokens to only a few split experts, Sparse
Mixture-of-Experts has enabled efficient training of large language models.
Recent findings suggest that fixing the routers can achieve competitive
performance by alleviating the collapsing problem, where all experts eventually
learn similar representations. However, this strategy has two key limitations:
(i) the policy derived from random routers might be sub-optimal, and (ii) it
requires extensive resources during training and evaluation, leading to limited
efficiency gains. This work introduces \HyperRout, which dynamically generates
the router's parameters through a fixed hypernetwork and trainable embeddings
to achieve a balance between training the routers and freezing them to learn an
improved routing policy. Extensive experiments across a wide range of tasks
demonstrate the superior performance and efficiency gains of \HyperRouter
compared to existing routing methods. Our implementation is publicly available
at {\url{{this https URL}}}.

通过固定的超网络和可训练嵌入层动态生成路由器参数的方式，HyperRouter 在训练和冻结过程中学习优化路由策略，从而在各项任务上展现出明显的性能提升和效率增益。