The field of diffusion models is rapidly advancing, with a focus on improving image and language generation capabilities. Recent developments have led to the creation of more efficient and effective models, such as those using multiplicative denoising score-matching and proximal diffusion neural samplers. These models have shown promising results in generating high-quality images and text, and have the potential to be used in a variety of applications. Noteworthy papers in this area include 'Hyperparameters are all you need' which proposes a training-free algorithm for generating high-quality images, and 'Dale meets Langevin' which introduces a biologically inspired generative model employing multiplicative updates. Additionally, 'Proximal Diffusion Neural Sampler' and 'Principled and Tractable RL for Reasoning with Diffusion Language Models' demonstrate the effectiveness of diffusion models in sampling and reinforcement learning tasks. Overall, the field of diffusion models is moving towards more innovative and advanced techniques, with a focus on improving efficiency, effectiveness, and applicability.