The case for studying AI models during training, not just afterward

In a June 2026 position paper, the authors argue that AI research should study how training produces model behavior instead of treating trained models as static artifacts. They call for theories that could predict outcomes early, guide interventions when training goes wrong and help design training procedures that more reliably produce desired properties. The paper also identifies open problems in interpretability, fairness, memorization and simplicity bias.