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Despite recent progress in improving the mathematical reasoning ability oflarge language models(LLMs), solving competition-level math problems withoutthe use of external tools remains challenging for open-source LLMs. In thiswork, we introduce the MMIQC dataset, a mixture of processed web data andsynthetic question-response pairs, to equip base models with bettermathematical reasoning skills. In different model sizes, the models fine-tunedon MMIQC consistently outperform their counterparts by a clear margin on MATHtest set. Notably, DeepSeek-67B-MMIQC achieves a 41.0% accuracy, 4.2% higherthan the previous open-source SOTA. Our experiments also show that a large partof the improvement can be attributed to our novel augmentation methodIQC(Iterative Question Composing), where we iteratively ask an LLM to composenew questions from the given seed problems and do rejection sampling fromanother LLM. MMIQC has now been released onhttps://huggingface.co/datasets/Vivacem/MMIQC.

Augmenting Math Word Problems via Iterative Question Composing