  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{{https://github.com/giangdip2410/HyperRouter}}}.
