Researchers have introduced DemoPSD, a novel approach to on-policy self-distillation (OPSD) that mitigates overfitting in large language models (LLMs) by modulating the teacher's supervision based on the student's disagreements. This method allows a single model to act as both teacher and student, with varying levels of information access, to improve reasoning capabilities. By conditioning the teacher's supervision on the student's uncertainties, DemoPSD reduces the model's tendency to overfit to in-domain patterns, leading to more robust and generalizable performance. The technique has significant implications for the development of more accurate and reliable LLMs, which can be applied to various domains, including natural language processing and decision-making tasks1. This breakthrough matters to practitioners because it enables the creation of more versatile and adaptable AI systems, which can operate effectively in diverse environments and scenarios, ultimately enhancing their potential impact on policy, security, and workforce dynamics.