Alain Durmus

Diffusion language models can now generate high-quality text with up to 49% fewer network evaluations by matching output distributions.

AM generates smooth trajectories efficiently, sidestepping costly preprocessing and simulation, while achieving competitive results against existing methods.

Improved KL and Wasserstein convergence bounds for Diffusion Flow Matching reveal state-of-the-art performance in high dimensions under minimal assumptions.