HERO-MoE: Historical Expert Routing with Scale-Preserving Fusion
Mixture-of-Experts (MoE) architectures have become a standard way to scale model capacity while keeping computation sparse, yet routing remains a key determinant of MoE quality and training behavior. Prior empirical studies suggest that MoE routing reflects input semantics and upstream computation across depth, but standard routers do not explicitly use the routing distributions produced by preceding layers. We propose HERO-MoE, Historical Expert ROuting with Scale-Preserving Fusion, a routing framework that injects historical routing priors into MoE routers by reusing detached, dense routing distributions collected from preceding MoE layers. The key idea is simple: HERO-MoE preserves the...
| Authors | Junxiang Qiu, Zhengsu Chen, Xinting Hu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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