KLASS: KL-Guided Fast Inference in Masked Diffusion Models

Seo Hyun Kim, H, Hojung Jung, Youngrok Park, Se-Young Yun

KLASS: KL-Guided Fast Inference in Masked Diffusion Models: 37 upvotes on Hugging Face Daily Papers, #3 of 14 papers on 2025-11-12. Day-by-day upvote history.

Masked diffusion models have demonstrated competitive results on various tasks including language generation. However, due to its iterative refinement process, the inference is often bottlenecked by slow and static sampling speed. To overcome this problem, we introduce `KL-Adaptive Stability Sampling' (KLASS), a fast yet effective sampling method that exploits token-level KL divergence to identify stable, high-confidence predictions. By unmasking multiple tokens in each iteration without any additional model training, our approach speeds up generation significantly while maintaining sample quality. On reasoning benchmarks, KLASS achieves up to 2.78times wall-clock speedups while improving performance over standard greedy decoding, attaining state-of-the-art results among diffusion-based samplers. We further validate KLASS across diverse domains, including text, image, and molecular generation, showing its effectiveness as a broadly applicable sampler across different models.

Paper page on Hugging Face · arXiv

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