KLASS: KL-Guided Fast Inference in Masked Diffusion Models
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.
6912e1b6a644ba07c499c82d
Seo Hyun Kim
claimed_verified
2025-11-12T12:21:05.693Z
62e167813eb0730f6217c95e
/avatars/95a18009f725832646643d6169c2bc08.svg
Seo Hyun Kim
shkim0116
user
shkim0116
6912e1b6a644ba07c499c82e
Sunwoo Hong
claimed_verified
2025-11-12T12:21:03.709Z
6487cd9d6d93bc7eb47ff207
/avatars/f0b33fafd3bed5e49fd4f6630be5ddf2.svg
H
SunwooHong
user
SunwooHong
6912e1b6a644ba07c499c82f
Hojung Jung
6912e1b6a644ba07c499c830
Youngrok Park
claimed_verified
2025-11-13T13:07:02.655Z
671532b6cf2b9210e588a38f
/avatars/40e3a445adcd6c30e1878e9db7ed588b.svg
Youngrok Park
yr-park
user
yr-park
6912e1b6a644ba07c499c831
Se-Young Yun
KLASS: KL-Guided Fast Inference in Masked Diffusion Models
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.
KL-Adaptive Stability Sampling (KLASS) accelerates diffusion-based generation by identifying stable predictions, achieving significant speedups and quality improvements across various domains.
masked diffusion models language generation KL divergence token-level sampling speed greedy decoding text generation image generation molecular generation