Policy Fine Tuning

Policy fine-tuning refines pre-trained models, leveraging existing data to adapt to new tasks efficiently. Current research focuses on methods like active learning to select optimal training data, aligning generative models with reward functions for continuous control, and using teacher-student frameworks for scalable large language model alignment. These advancements improve sample efficiency and reduce the need for extensive human annotation, impacting fields like robotics and natural language processing by enabling faster and more robust adaptation of AI agents to diverse scenarios.