WarriorMath：利用缺陷感知框架增强大语言模型的数学能力
WarriorMath: Enhancing the Mathematical Ability of Large Language Models with a Defect-aware Framework
AI总结：
针对现有数据增强忽视模型失败模式的问题，提出缺陷感知框架WarriorMath，通过多专家协作合成缺陷数据并渐进训练，在六个数学基准上平均提升12.57%。
摘要：
大语言模型（LLMs）在解决数学问题方面表现出色，但其性能往往受到高质量、多样化训练数据可用性的限制。现有方法侧重于通过改写或难度递进来增强数据集，却忽视了大语言模型的具体失败模式。这导致生成的合成问题模型已经能够解决，从而带来的性能提升微乎其微。为了解决这一问题，我们提出了WarriorMath，一个用于数学问题求解的缺陷感知框架，该框架整合了针对性的数据合成与渐进式训练。在合成阶段，我们采用多个专家大语言模型进行协作，以生成、批判并改进问题。基础大语言模型未能解决的问题会被识别出来，并通过专家级反馈进行迭代改进，从而产生高质量、缺陷感知的训练数据。在训练阶段，我们引入了一个渐进式学习框架，该框架利用针对模型弱点量身定制的、难度递增的数据对模型进行迭代微调。在六个数学基准上的实验表明，WarriorMath平均比强基线高出12.57%，创下了新的最先进水平。我们的结果证明了缺陷感知、多专家框架在提升数学能力方面的有效性。
英文摘要：
Large Language Models (LLMs) excel in solving mathematical problems, yet their performance is often limited by the availability of high-quality, diverse training data. Existing methods focus on augmenting datasets through rephrasing or difficulty progression but overlook the specific failure modes of LLMs. This results in synthetic questions that the model can already solve, providing minimal performance gains. To address this, we propose WarriorMath, a defect-aware framework for mathematical problem solving that integrates both targeted data synthesis and progressive training. In the synthesis stage, we employ multiple expert LLMs in a collaborative process to generate, critique, and refine problems. Questions that base LLMs fail to solve are identified and iteratively improved through expert-level feedback, producing high-quality, defect-aware training data. In the training stage, we introduce a progressive learning framework that iteratively fine-tunes the model using increasingly challenging data tailored to its weaknesses. Experiments on six mathematical benchmarks show that WarriorMath outperforms strong baselines by 12.57% on average, setting a new state-of-the-art. Our results demonstrate the effectiveness of a defect-aware, multi-expert framework for improving mathematical ability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
