Author

Eric Moulines

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#machine learning Preprint Oct 2026

One-Step Generation via Riemannian Wasserstein Gradient Flows

Recently, Drifting Models and Wasserstein Gradient Flows have attracted substantial attention because they move iterative distributional refinement to training and amortize it into a generator, enabling fast inference. However, existing formulations have been developed largely for continuous Euclidean domains, such as...

David Li, Chanhyuk Lee, Jaehoon Yoo et al. · 0 citations

#machine learning Preprint Oct 2026

IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models

Masked discrete diffusion models offer a promising alternative to autoregressive generation, but iterative sampling can be costly, and intractable sequence likelihoods complicate reward fine-tuning. We introduce IDRF, a framework for reward fine-tuning of few-step masked discrete diffusion generators. Starting from a s...

V. Gromadskii, David Li, Samson Gourevitch et al. · 0 citations