ICML 2026 Oral Paper: A Science of AI Must Study Training Dynamics
max_paperclips · x · 2026-08-13
This position paper, accepted as an oral at ICML 2026, argues that current AI research treats models as static artifacts, relying too heavily on post-hoc analysis and fixes. It advocates that a true science of AI must study the training dynamics that produce model behavior.
The authors suggest that scientific understanding should support predicting outcomes from early training signals, intervening when trajectories go wrong, and designing training procedures that reliably yield desired properties. While scaling laws have made loss prediction routine, the challenge remains to extend this success to capabilities, biases, robustness, and safety. The paper also examines progress in mechanistic interpretability, fairness, memorization, and simplicity bias through the lens of the history and philosophy of science, identifying concrete open problems.
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