Reward Fine Tuning

Reward fine-tuning refines pre-trained generative models, such as diffusion models and language models, by optimizing them to maximize a reward signal reflecting desired outputs (e.g., human preferences, image quality metrics). Current research focuses on developing efficient and stable algorithms, including those based on stochastic optimal control, reward prediction, and direct gradient-based methods, to address challenges like instability in large-scale training and the need for effective multi-objective optimization. This area is significant because it enables the alignment of powerful generative models with human values and specific application needs, improving the quality, safety, and ethical implications of AI-generated content across diverse domains.