Repetition of training data produces a systematic eval loss peak at intermediate repeat counts whose location scales with model size, quantifiable as large compute-equivalent loss even at modest repetition fractions.
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Internal Data Repetition Destroys Language Models
Repetition of training data produces a systematic eval loss peak at intermediate repeat counts whose location scales with model size, quantifiable as large compute-equivalent loss even at modest repetition fractions.