Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking

• URL: http://arxiv.org/abs/2505.24857v1
• Date: Fri, 30 May 2025 17:52:55 GMT
• Title: Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking
• Authors: Heli Ben-Hamu, Itai Gat, Daniel Severo, Niklas Nolte, Brian Karrer,
• Abstract summary: Masked diffusion models (MDMs) have shown competitive performance compared to autoregressive models (ARMs) for language modeling.<n>We introduce EB-Sampler, a drop-in replacement for existing samplers, utilizing an Entropy Bounded unmasking procedure.<n> EB-Sampler accelerates sampling from current state of the art MDMs by roughly 2-3x on standard coding and math reasoning benchmarks without loss in performance.
• Score: 17.511240770486452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent masked diffusion models (MDMs) have shown competitive performance compared to autoregressive models (ARMs) for language modeling. While most literature has focused on performance enhancing sampling procedures, efficient sampling from MDMs has been scarcely explored. We make the observation that often a given sequence of partially masked tokens determines the values of multiple unknown tokens deterministically, meaning that a single prediction of a masked model holds additional information unused by standard sampling procedures. Based on this observation, we introduce EB-Sampler, a simple drop-in replacement for existing samplers, utilizing an Entropy Bounded unmasking procedure that dynamically unmasks multiple tokens in one function evaluation with predefined approximate error tolerance. We formulate the EB-Sampler as part of a broad family of adaptive samplers for which we provide an error analysis that motivates our algorithmic choices. EB-Sampler accelerates sampling from current state of the art MDMs by roughly 2-3x on standard coding and math reasoning benchmarks without loss in performance. We also validate the same procedure works well on smaller reasoning tasks including maze navigation and Sudoku, tasks ARMs often struggle with.

Related papers

• Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and Applications [79.53938312089308]
  The MIDX-Sampler is a novel adaptive sampling strategy based on an inverted multi-index approach.<n>Our method is backed by rigorous theoretical analysis, addressing key concerns such as sampling bias, gradient bias, convergence rates, and generalization error bounds.
  arXiv  Detail & Related papers  (2025-01-15T04:09:21Z)
• Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling [47.82616476928464]
  Masked diffusion models (MDMs) have emerged as a popular research topic for generative modeling of discrete data.<n>We show that both training and sampling of MDMs are theoretically free from the time variable.<n>We identify, for the first time, an underlying numerical issue, even with the commonly used 32-bit floating-point precision.
  arXiv  Detail & Related papers  (2024-09-04T17:48:19Z)
• Autoregressive Speech Synthesis without Vector Quantization [135.4776759536272]
  We present MELLE, a novel continuous-valued token based language modeling approach for text-to-speech synthesis (TTS)<n>MELLE autoregressively generates continuous mel-spectrogram frames directly from text condition.<n>MELLE mitigates robustness issues by avoiding the inherent flaws of sampling vector-quantized codes.
  arXiv  Detail & Related papers  (2024-07-11T14:36:53Z)
• Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation [49.827306773992376]
  Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions. Our proposed method attains state-of-the-art performance in both classification and segmentation CTTA tasks.
  arXiv  Detail & Related papers  (2023-12-19T15:34:52Z)
• Masked Generative Modeling with Enhanced Sampling Scheme [1.3927943269211591]
  Enhanced Sampling Scheme (ESS) ensures both sample diversity and fidelity. ESS consists of three stages: Naive Iterative Decoding, Critical Reverse Sampling, and Critical Resampling. We demonstrate significant performance gains of ESS in both unconditional sampling and class-conditional sampling.
  arXiv  Detail & Related papers  (2023-09-14T09:42:13Z)
• AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models [103.41269503488546]
  Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models with user-provided concepts. This paper aims to address the challenge of DPM customization when the only available supervision is a differentiable metric defined on the generated contents. We propose a novel method AdjointDPM, which first generates new samples from diffusion models by solving the corresponding probability-flow ODEs. It then uses the adjoint sensitivity method to backpropagate the gradients of the loss to the models' parameters.
  arXiv  Detail & Related papers  (2023-07-20T09:06:21Z)
• CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models [72.93652777646233]
  Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. We propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask.
  arXiv  Detail & Related papers  (2023-05-29T07:49:44Z)
• Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction [8.44194619347218]
  Undersampling is a common method in Magnetic Resonance Imaging (MRI) to subsample the number of data points in k-space. We propose a method that directly learns the undersampling masks from data points. We show that different anatomic regions reveal distinct optimal undersampling masks.
  arXiv  Detail & Related papers  (2023-05-25T14:42:04Z)
• MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation [31.648523213206595]
  Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. We propose a novel approach, dubbed MaskDiff, which models the underlying conditional distribution of a binary mask.
  arXiv  Detail & Related papers  (2023-03-09T08:24:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.