Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics

This position paper argues for a scientific understanding of AI that focuses on studying training dynamics, rather than just analyzing models post-training. It emphasizes predicting outcomes, intervening when issues arise, and designing training procedures to reliably produce desired properties, extending the success of scaling laws beyond loss to capabilities, biases, robustness, and safety.