Tony discusses the integration of temporal consistency into survival models, highlighting its significance in improving estimators. By ensuring that survival estimates remain consistent over time, the models achieve greater accuracy, bridging the gap between survival modeling and reinforcement learning concepts. This evolution showcases a deeper sophistication in applying RL techniques to real-world data.
