World Modeling

World modeling aims to create computational representations of environments, enabling agents to predict future states and plan actions effectively. Current research focuses on improving the efficiency and generalization of these models, particularly using transformer-based architectures and techniques like contrastive learning and diffusion models, often within model-based reinforcement learning frameworks. These advancements are driving progress in robotics, autonomous systems, and AI safety by enabling more robust and sample-efficient learning in complex, dynamic environments. Furthermore, research is exploring how to better evaluate and compare the capabilities of different world models, focusing on aspects like knowledge representation and generalization to unseen tasks.