Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics
Quick Answer
This paper shows that This position paper emphasizes the need for a scientific understanding of AI, advocating for the study of training dynamics rather than treating models as static artifacts.
Quick Take
This position paper emphasizes the need for a scientific understanding of AI, advocating for the study of training dynamics rather than treating models as static artifacts. It stresses the importance of predicting outcomes from early training signals and designing training procedures that reliably produce desired properties, addressing challenges in capabilities, biases, and safety-relevant behaviors.
Key Points
- AI models are dynamic, shaped by data and optimization processes.
- Current research often overlooks the importance of training dynamics.
- The paper calls for predicting outcomes from early training signals.
- Challenges include extending success in loss prediction to biases and safety.
- Open problems in mechanistic interpretability and fairness are identified.
Article Excerpt
From source RSS / original summaryarXiv:2606. 06533v1 Announce Type: new Abstract: What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization dynamics. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge.
This position paper argues that a science of AI must move beyond post-hoc fixes and study the training dynamics that produce model behavior. Such a science should support progressively stronger forms of understanding: predicting outcomes from early training signals, intervening when trajectories go wrong, and ultimately designing training procedures that more reliably produce desired properties.
Scaling laws have made prediction routine for loss; the challenge is extending this success to capabilities, biases, robustness, and safety-relevant behaviors. We articulate requirements for such theories grounded in the history and philosophy of science, examine progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and identify concrete open problems.
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