Expert Distribution

Expert distribution research focuses on leveraging demonstrations or preferences from experts to train models, particularly in areas like language modeling and robotics. Current efforts concentrate on developing efficient algorithms, such as soft preference optimization and diffusion model-augmented behavioral cloning, to learn from expert data, often addressing challenges like data scarcity and catastrophic forgetting through techniques like mixture-of-experts models and knowledge distillation. This work is significant for improving the performance and sample efficiency of machine learning models, leading to better generalization and applicability across diverse domains, including healthcare and control systems.