Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models
DEMASK predicts token dependencies in discrete diffusion language models to select weakly dependent masked positions for parallel unmasking, bounding sampling error and accelerating Dream-7B by 1.7, 2.2× with preserved accuracy.
Published 2026Sydney Poster Session 6 · Thu, Dec 10, 5:00 PM–8:00 PM local time · Hall 1-4arXiv ↗OpenReview ↗
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Abstract
Discrete diffusion language models (dLLMs) accelerate text generation by unmasking multiple tokens in parallel. However, parallel decoding introduces a distributional mismatch: it approximates the joint conditional using a fully factorized product of per-token marginals, which degrades output quality when selected tokens are strongly dependent. We propose DEMASK (DEpendency-guided unMASKing), a lightweight dependency predictor that attaches to the final hidden states of a dLLM. In a single forward pass, it estimates pairwise conditional influences between masked positions. Using these predictions, a greedy selection algorithm identifies positions with bounded cumulative dependency for simultaneous unmasking. Under a sub-additivity assumption, we prove this bounds the total variation distance between our parallel sampling and the model's joint. Empirically, DEMASK achieves 1.7-2.2$\times$ speedup on Dream-7B while matching or improving accuracy compared to confidence-based and KL-based baselines.