Preference Pair

Preference pairs, consisting of two options where one is preferred over the other, are fundamental data for aligning artificial intelligence models with human values. Current research focuses on developing efficient and robust methods for learning from these pairs, moving beyond simple reward modeling towards richer representations that capture complex preference structures, often employing algorithms like Direct Preference Optimization (DPO) and its variants. This work is crucial for improving the safety and reliability of AI systems by enabling better alignment with human intentions across diverse applications, from language models to image generation and beyond.