ICML oral: an AI science must study training dynamics, not just fix models in post
ChrisGPotts · x · 2026-10-23
A position paper by Stella Biderman, Mohammad Aflah Khan, Niloofar Mireshghallah, Naomi Saphra and colleagues (ICML oral) argues that models are snapshots of time-evolving processes, yet much AI research treats them as fixed artifacts and analyzes behavior post hoc. The paper calls for a science of AI grounded in training dynamics — predicting outcomes from early training signals, intervening when trajectories go wrong, and designing training procedures that reliably yield desired properties — reviewing progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and listing concrete open problems.
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