Pre-Memorization Train Accuracy Reliably Predicts Generalization in LLM Reasoning
Keywords: LLMs, Generalization, Memorization, Reasoning
Abstract: When large language models (LLMs) are finetuned on reasoning tasks, they can either reduce their training loss by developing problem-solving abilities, or by simply memorizing target traces in the training data. Our work aims to better understand how this learning process shapes a model's ability to generalize. We observe that, while LLMs often perfectly memorize most target solution traces by the end of training, their predictions at intermediate checkpoints can provide valuable insights into their behavior at test time. Concretely, we introduce the concept of pre-memorization train accuracy: the accuracy of model samples for training queries prior to exactly reproducing reasoning traces in the training data. We find that the average pre-memorization train accuracy of the model is strongly predictive of its test performance, with coefficients of determination around or exceeding 0.9 across various models (Llama3-8B, Gemma2-9B), datasets (GSM8k, MATH), and training setups. Beyond this aggregate statistic, we find that the pre-memorization train accuracy of individual examples can predict the model’s sensitivity to input perturbations for those examples, allowing us to identify examples for which the model fails to learn robust solutions. A natural application of this insight is in data curation. We find that prioritizing the collection of examples with low pre-memorization accuracy leads to 1.5-2x data efficiency compared to i.i.d. data scaling, and outperforms other standard data curation techniques.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 12592
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