Preprint

Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling. …

Distribution Matching Distillation (DMD) compresses pretrained diffusion models into efficient few-step generators by aligning their …

Flow and diffusion models generate high-quality samples in many modalities; however, many network evaluations are required during …

For a fixed flow-based generative model under a small inference budget, sample quality can depend strongly on where the sampler spends …

Generative modeling over discrete structures underpins applications across deep learning, from biological sequence design and code …

Diffusion language models (DLMs) have recently demonstrated capabilities that complement standard autoregressive (AR) models, …

Reward alignment for flow and diffusion models, via test-time steering or fine-tuning, often relies on sampling from conditional …

Although diffusion models now occupy a central place in generative modeling, introductory treatments commonly assume Euclidean data and …