Hi, the "Flow Matching paradigm" a simulation-free approach for training Continuous Normalizing Flows (CNFs) based on regressing vector fields of fixed conditional probability paths (original paper: https://arxiv.org/abs/2210.02747, MIT Course with practical examples: https://diffusion.csail.mit.edu/2026/index.html). Less formally Flow Matching can be described as "a technique to learn how to transport samples from one distribution to another". For example we could learn how to transport samples from a simple distribution we can easily sample from (e.g. Gaussian) to a complex distribution (e.g. images , videos , robot actions , etc.).