通过数据集分解学习解决复杂问题
Learning to Solve Complex Problems via Dataset Decomposition
AI总结：
通过数据集分解和课程生成方法，提升模型解决复杂问题的能力
摘要：
课程学习是一种训练策略，通过难度对模型所接触的数据进行组织，从简单的例子逐渐过渡到更复杂的例子。本研究探索了一种反向课程生成方法，通过递归地将复杂数据集分解为更简单、更易学习的组件。我们提出了一种教师-学生框架，其中教师具备逐步推理的能力，用于递归地生成更简单的例子版本，使学生模型逐步掌握困难任务。我们提出了一种新的评分系统，基于数据的结构复杂性和概念深度来衡量数据难度，允许在分解数据上构建课程。在数学数据集（MATH和AIME）和代码生成数据集上的实验表明，使用我们方法生成的课程训练模型在性能上优于标准训练原始数据集的模型。
英文摘要：
Curriculum learning is a class of training strategies that organizes the data being exposed to a model by difficulty, gradually from simpler to more complex examples. This research explores a reverse curriculum generation approach that recursively decomposes complex datasets into simpler, more learnable components. We propose a teacher-student framework where the teacher is equipped with the ability to reason step-by-step, which is used to recursively generate easier versions of examples, enabling the student model to progressively master difficult tasks. We propose a novel scoring system to measure data difficulty based on its structural complexity and conceptual depth, allowing curriculum construction over decomposed data. Experiments on math datasets (MATH and AIME) and code generation datasets demonstrate that models trained with curricula generated by our approach exhibit superior performance compared to standard training on original datasets.
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。
