Preference Learning with Response Time

This paper introduces methods to incorporate response time data into human preference learning, enhancing reward model accuracy and efficiency. By leveraging the Evidence Accumulation Drift Diffusion model and developing Neyman-orthogonal loss functions, the approach improves sample efficiency and reduces error rates, with validated experiments on image preference tasks.