Preference Reward

Preference reward research focuses on aligning artificial intelligence models, particularly large language models and generative models, with human preferences by using feedback to optimize their outputs. Current research emphasizes developing robust reward models that capture the nuanced and often conflicting nature of human preferences, employing techniques like quantile regression and distributional reward estimation to move beyond simple scalar rewards. This work is crucial for improving the safety, reliability, and overall user experience of AI systems across diverse applications, from text generation and image synthesis to complex problem-solving and multi-agent interactions.