Cluster-Level Attention-Guided Parallel Decoding for Masked Diffusion Language Models
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Computer Science > Machine Learning
Title:Cluster-Level Attention-Guided Parallel Decoding for Masked Diffusion Language Models
Abstract:Masked diffusion language models (MDLMs) enable parallel decoding by predicting all masked positions at each denoising step, yet existing training-free samplers usually decide which positions to commit at token-level granularity. We revisit this granularity and observe that reliable predictions often emerge as contiguous high-confidence spans, suggesting that the unit of parallel commitment can be larger than a single token. We first group adjacent high-confidence candidates into confidence-induced clusters (CICs) as span-level update units. We then use self-attention maps from the same forward pass to estimate inter-cluster dependencies, enabling conflict-aware selection of mutually compatible CICs for parallel commitment. This yields CLAD (Cluster-Level Attention-Guided Decoding), a training-free cluster-level decoder for MDLMs. Experiments on LLaDA and Dream model families across four reasoning and code-generation benchmarks show that CLAD achieves 1.77x--8.47x speedups over Vanilla decoding while maintaining broadly comparable task accuracy in most settings.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.29607 [cs.LG] |
| (or arXiv:2605.29607v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29607
arXiv-issued DOI via DataCite (pending registration)
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