Small-scale models offer various computational advantages, and yet to which extent size is critical for problem-solving abilities remains an open question. Specifically for solving grade school math, the smallest model size so far required to break the 80% barrier on the GSM8K benchmark remains to be 34B. Our work studies how high-quality datasets may be the key for small language models to acquire mathematical reasoning. We introduce \\(\\texttt{TinyGSM}\\), a synthetic dataset of 12.3M grade school math problems paired with Python solutions, generated fully by GPT-3.5. After finetuning on \\(\\texttt{TinyGSM}\\), we find that a duo of a 1.3B generation model and a 1.3B verifier model can achieve 81.5% accuracy, outperforming existing models that are orders of magnitude larger. This also rivals the performance of the GPT-3.5 “teacher” model (77.4%), from which our model’s training data is generated. Our approach is simple and has two key components: 1) the high-quality dataset \\(\\texttt{TinyGSM}\\), 2) the use of a verifier, which selects the final outputs from multiple candidate generations.<\/p>\n","protected":false},"excerpt":{"rendered":"
Small-scale models offer various computational advantages, and yet to which extent size is critical for problem-solving abilities remains an open question. Specifically for solving grade school math, the smallest model size so far required to break the 80% barrier on the GSM8K benchmark remains to be 34B. Our work studies how high-quality datasets may be 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