Expert-Choice Routing Enables Adaptive Computation in Diffusion Language Models
Diffusion language models (DLMs) enable parallel, non-autoregressive text generation, yet existing DLM mixture-of-experts (MoE) models inherit token-choice (TC) routing from autoregressive systems, leading to load imbalance and rigid computation allocation. We show that expert-choice (EC) routing is a better fit for DLMs: it provides deterministic load balancing by design, yielding higher throughput and faster convergence than TC. Building on the property that EC capacity is externally controllable, we introduce timestep-dependent expert capacity, which varies expert allocation according to the denoising step. We find that allocating more capacity to low-mask-ratio steps consistently achi...
| Authors | Shuibai Zhang, Caspian Zhuang, Chihan Cui et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 3 August 2026 |
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