Why Human vs. AI Sample Efficiency Comparisons Are Misleading

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Debates over how much data AI models need compared to children hinge entirely on what researchers choose to count, making any single ratio more a reflection of methodology than reality. A child's learning input extends far beyond words to include objects, faces, actions, and causally structured experiences, meaning word-count comparisons capture only a fraction of human input. The comparison is further complicated by the lack of a fixed benchmark, since a child and a language model do not demonstrate competence in the same ways or on the same tasks. Whether evolutionary optimization should be counted as a form of pretraining for humans is a genuinely unresolved question that can shift the result by orders of magnitude. Researchers have identified at least five distinct ways to frame the comparison, each measuring a different quantity, and conflating them is the primary source of confusion in the field.

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ProgrammingDEV Community ·

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