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More Data Often Raises Training Error

As the training set grows, development and test error usually move downward, but the error measured on the training set often moves upward. With a very small dataset, a model can often memorize the examples and achieve nearly perfect training performance. When the dataset becomes larger, more varied, or noisier, fitting every example exactly becomes harder, so training error tends to increase.

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Updated 2026-08-12

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