Mean-Field Parallel Decoding for Discrete Diffusion Language Models
Discrete diffusion language models enable parallel token generation, offering a pathway to low-latency decoding. However, selecting tokens independently by marginal confidence limits effective parallelism: tokens that appear reliable in isolation can form incompatible configurations when several positions are updated at once. We introduce a training-free decoding framework that coordinates these parallel updates. At each forward pass, the method assigns a commit score to each masked position and refines these scores using pairwise interactions derived from the model's predictive distributions. A variational relaxation yields a simple fixed-point update that suppresses conflicting simultan...
| Authors | Tamim Zoabi, Ameen Ali, Liran Ringel et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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