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

A position paper argues that AI research must shift from analyzing finished models to studying the training dynamics that produce model behaviors. The authors propose that a rigorous science of AI requires understanding how data, objectives, and optimization shape model properties—enabling prediction and intervention during training rather than post-hoc fixes.

This position paper addresses a fundamental gap in AI research methodology: the tendency to treat trained models as static objects rather than examining the dynamic processes that create them. The authors contend that meaningful scientific understanding requires predicting and controlling outcomes during training, not merely explaining behaviors after the fact. This represents a paradigm shift from reactive debugging to proactive system design.

The current AI research landscape relies heavily on scaling laws for predicting loss metrics, yet lacks comparable frameworks for anticipating capabilities, biases, robustness, and safety properties. This asymmetry creates blind spots when deploying models at scale. The paper grounds its argument in philosophy of science principles, asserting that genuine understanding requires mechanistic insight into how training dynamics produce emergent behaviors. Recent progress in mechanistic interpretability, fairness research, and memorization studies demonstrates feasibility, though substantial work remains.

For the AI development community, this framework has immediate practical implications. Understanding training dynamics enables intervention when trajectories diverge from desired outcomes, reducing costly retraining cycles and improving safety alignment. For practitioners building production systems, the ability to predict safety-relevant behaviors from early signals could substantially reduce deployment risks. The framework also positions mechanistic interpretability as essential infrastructure rather than academic curiosity.

Looking forward, the field must develop mathematical theories connecting training procedures to behavioral outcomes across multiple dimensions simultaneously. This requires interdisciplinary collaboration between theorists, empiricists, and domain experts in fairness and safety. Success here would fundamentally reshape how AI systems are developed, validated, and deployed.

• →AI research must study training dynamics instead of analyzing models as static post-training artifacts
• →Mechanistic understanding of training processes enables prediction and intervention rather than reactive fixes
• →Scaling laws for loss prediction must be extended to safety, fairness, and capability dimensions
• →A scientific approach to AI requires grounding in philosophy of science and systematic theory-building
• →Early training signals could predict final model behaviors, reducing deployment risks and retraining costs