The field of generative learning is witnessing a significant shift towards the adoption of diffusion models and optimal transport techniques. Recent research has focused on reinterpreting diffusion models through the lens of Wasserstein Gradient Flow, providing a more principled and elegant framework for understanding these models. Additionally, the development of differentiable Expectation-Maximisation algorithms has enabled the integration of optimal transport distances into modern learning pipelines. These advances have far-reaching implications for applications in image and video generation, finance, and reinforcement learning. Noteworthy papers in this area include: Are We Really Learning the Score Function, which challenges the conventional understanding of diffusion models, and Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport, which introduces a novel approach to computing optimal transport distances. Furthermore, papers such as Coefficients-Preserving Sampling for Reinforcement Learning with Flow Matching and BranchGRPO: Stable and Efficient GRPO with Structured Branching in Diffusion Models have made significant contributions to the development of more efficient and stable reinforcement learning algorithms for diffusion models.