Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models

• URL: http://arxiv.org/abs/2604.02560v1
• Date: Thu, 02 Apr 2026 22:21:24 GMT
• Title: Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models
• Abstract summary: We propose DEMASK, a lightweight dependency predictor that attaches to the final hidden states of a dLLM.<n> 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.
• Score: 21.699371484195865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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