{"slug": "mathmixup-boosting-llm-mathematical-reasoning-via-curriculum-and-decomposition", "title": "MathMixup: Boosting LLM Mathematical Reasoning via Curriculum and Decomposition", "summary": "A paper submitted to arXiv on 14 January 2026 introduces MathMixup, a data synthesis paradigm that generates difficulty-controllable mathematical reasoning problems through hybrid and decomposed strategies, paired with a curriculum learning approach built on the resulting MathMixupQA dataset. The authors report that a fine-tuned Qwen2.5-7B model achieves an average score of 52.6% across seven mathematical benchmarks, surpassing previous state-of-the-art methods. The work targets the limited diversity and imprecise difficulty control of existing synthesis methods, which the authors say undermines curriculum learning for LLM math training.", "body_md": "# Computer Science > Machine Learning\n\n  [Submitted on 14 Jan 2026]\n\n# Title:MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning\n\n[View PDF](https://arxiv.org/pdf/2601.17006)\n\n[HTML (experimental)](https://arxiv.org/html/2601.17006v1)\n\nAbstract:In mathematical reasoning tasks, the advancement of Large Language Models (LLMs) relies heavily on high-quality training data with clearly defined and well-graded difficulty levels. However, existing data synthesis methods often suffer from limited diversity and lack precise control over problem difficulty, making them insufficient for supporting efficient training paradigms such as curriculum learning. To address these challenges, we propose MathMixup, a novel data synthesis paradigm that systematically generates high-quality, difficulty-controllable mathematical reasoning problems through hybrid and decomposed strategies. Automated self-checking and manual screening are incorporated to ensure semantic clarity and a well-structured difficulty gradient in the synthesized data. Building on this, we construct the MathMixupQA dataset and design a curriculum learning strategy that leverages these graded problems, supporting flexible integration with other datasets. Experimental results show that MathMixup and its curriculum learning strategy significantly enhance the mathematical reasoning performance of LLMs. Fine-tuned Qwen2.5-7B achieves an average score of 52.6\\% across seven mathematical benchmarks, surpassing previous state-of-the-art methods. These results fully validate the effectiveness and broad applicability of MathMixup in improving the mathematical reasoning abilities of LLMs and advancing data-centric curriculum learning.\n    \n\n### Additional Features\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/mathmixup-boosting-llm-mathematical-reasoning-via-curriculum-and-decomposition", "canonical_source": "https://arxiv.org/abs/2601.17006", "published_at": "2026-10-27 20:03:51+00:00", "updated_at": "2026-10-27 20:11:37.684851+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": ["MathMixup", "MathMixupQA", "Qwen2.5-7B", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/mathmixup-boosting-llm-mathematical-reasoning-via-curriculum-and-decomposition", "markdown": "https://wpnews.pro/news/mathmixup-boosting-llm-mathematical-reasoning-via-curriculum-and-decomposition.md", "text": "https://wpnews.pro/news/mathmixup-boosting-llm-mathematical-reasoning-via-curriculum-and-decomposition.txt", "jsonld": "https://wpnews.pro/news/mathmixup-boosting-llm-mathematical-reasoning-via-curriculum-and-decomposition.jsonld"}}