Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning

Understand

Supervised fine-tuning enhances the problem-solving abilities of language models across various mathematical reasoning tasks.

• To maximize such benefits, existing research focuses on broadening the training set with various data augmentation techniques, which is effective for standard single-round question-answering settings.
• Our work introduces a novel technique aimed at cultivating a deeper understanding of the training problems at hand, enhancing performance not only in standard settings but also in more complex scenarios that require reflective thinking.
• Specifically, we propose reflective augmentation, a method that embeds problem reflection into each training instance.