SpecDrop: Parameter-Free Category-Conditioned Routing for Modular Specialization
Mixture-of-experts (MoE) networks pursue specialization through learned routers, gates, and load-balancing losses, yet at matched total-parameter budgets learned routers can underperform equal-weight No-Routing baselines. Is the bottleneck the routing algorithm, or the alignment between training-signal granularity and the target categories? We probe the question with SpecDrop, a fixed parameter-free routing scheme: each of $K$ branches receives weight $p_a$ for its assigned category and a small leakage $p_i > 0$ otherwise, merged through a category-independent fixed denominator, with no learned routing parameters and no auxiliary losses; the category label is required at inference. On vis...
| Authors | Boyao Wang, Zhihan Lei |
|---|---|
| Primary category | cs.LG |
| Published | 4 August 2026 |
| Last updated at source | 27 September 2026 |
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