A new approach in reinforcement learning from human feedback (RLHF) aims to improve model robustness by dynamically adjusting the rationality parameter based on cognitive biases in human judgments. This method enhances the reliability of reward models, potentially leading to more accurate AI outputs even when trained on biased data.