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Popular Mechanistic Interpretability: Goodfire Lights the Way to AI Safety

"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

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Regularization Resists Memorization

Regularization techniques, including L1 and L2 norms, aim to prevent models from memorizing data. In modern foundation model training, memorization is inherently limited because data points are typically seen only once due to effective deduplication practices. Instead of memorizing specific data, models must learn generalizable features that can be reused across various tasks. Given that memorization does not contribute to model utility, the available capacity is better allocated to developing functional circuits. However, in scenarios like grokking, with limited data points, extensive training through multiple epochs is needed to facilitate model adaptation, creating a need for vigorous regularization to counteract potential memorization.

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