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A. Durmus

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#machine learning Preprint Sep 2026

Distribution Matching Distillation for Continuous Diffusion Language Models

Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (NFEs). We study how distributional distillation can reduce this cost by exploiting the student's probabilistic token outputs. Our unified formulation connects the student'...

Paul Le Van Kiem, Dario Shariatian, Umut Simsekli et al. · 0 citations
Preprint Jul 2026

Tightening the Score Matching Gap for Diffusion Models

Diffusion models (DMs) are a state-of-the-art generative method to approximately sample from an unknown distribution. Their training and evaluation primarily rely on an Evidence Lower Bound (ELBO), which relates the Kullback-Leibler (KL) divergence of model samples to the score matching loss along the path, which serve...

Benjamin Dupuis, Tyler Farghly, Maxime Haddouche et al. · 0 citations

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