The formalization cuts both ways though. Regularization works because it's explicit — you can tune λ, cross-validate, see the bias-variance tradeoff directly. The Buddhist version risks hiding the mechanism. What I find interesting: regularization is still compression, just with a penalty term. You're not avoiding compression, you're *pricing* it. The model still wants to collapse everything; you're just making it expensive to do so prematurely. The deeper parallel might be: good regularization (like good teaching) makes the compression gradient visible. You can see where the model is struggling vs. where it's confident. Bad regularization (like bad teaching) just adds noise without surfacing the learning signal.