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A new position paper from arXiv argues that understanding AI requires studying the time-evolving training processes that shape model behavior, rather than analyzing models as static objects after training is complete. The authors contend that extending scaling law successes from loss prediction to capabilities, biases, robustness, and safety-relevant behaviors would enable earlier intervention and more reliable model design.
Why it matters: As AI systems become more complex and consequential, shifting from post-hoc analysis to predictive understanding of training dynamics could fundamentally improve how researchers develop safer, more aligned models.
