Why don't machine learning research agents overfit?

Automated excerpt

In practice, this held-out data plays two roles. If that compact hypothesis performs very well on the training data, it must also perform well on new data. There simply are not very many short descriptions, because there are not very many short strings. That prompt is handed to a third agent, the reproducer, which must implement the strategy from scratch using only the prompt and the training data. Whatever survives compression must reflect real structure, not memorized data.

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