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This paper demonstrates that expert-choice (EC) routing, unlike token-choice (TC) routing, is a better fit for diffusion language models (DLMs) due to its deterministic load balancing, leading to higher throughput and faster convergence. They further introduce timestep-dependent expert capacity, allocating more capacity to low-mask-ratio steps based on the finding that these steps exhibit higher learning efficiency. Experiments show that retrofitting existing TC DLMs with EC routing improves convergence and accuracy across downstream tasks, establishing EC routing as a superior paradigm for DLM MoE models.
Diffusion language models can achieve faster convergence and improved accuracy simply by swapping token-choice routing for expert-choice routing, and further benefit from allocating more compute to early denoising steps.
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 achieves the best performance under matched FLOPs, and provide a mechanistic explanation: tokens in low-mask-ratio contexts exhibit an order-of-magnitude higher learning efficiency, so concentrating compute on these steps yields the largest marginal return. Finally, we show that existing pretrained TC DLMs can be retrofitted to EC by replacing only the router, achieving faster convergence and improved accuracy across diverse downstream tasks. Together, these results establish EC routing as a superior paradigm for DLM MoE models and demonstrate that computation in DLMs can be treated as an adaptive policy rather than a fixed architectural constant. Code is available at https://github.com/zhangshuibai/EC-DLM.
