Researchers have introduced Diffusion Augmented Agents (DAAG), a novel framework aimed at improving sample efficiency and transfer learning in reinforcement learning. The framework leverages large language models, vision language models, and diffusion models to enhance exploration and learning capabilities. This method, developed by Palo et al., also facilitates fast autonomy transfer, representing a significant advancement in the field of artificial intelligence, particularly in the application of deep learning and machine learning techniques.