Large language models (LLMs) have revolutionized how we interact with technology, demonstrating remarkable abilities in understanding and generating human language. However, these impressive models often stumble when faced with complex reasoning tasks, especially in mathematics. Why? One key reason is the lack of large, high-quality datasets specifically designed to train LLMs in mathematical reasoning. A new approach, Template-based Data Generation (TDG), aims to solve this problem. Imagine having a tool that can create an almost endless supply of math problems and their solutions. TDG does just that. By using parameterized templates, it generates diverse and challenging math problems, along with their solutions in both code and natural language. This is like having a virtual math tutor that can create customized practice problems on demand. What's even more innovative is that TDG uses another LLM (like GPT-4) to create these templates. This ensures the problems are varied and complex, pushing the boundaries of what LLMs can learn. Researchers have used TDG to build a massive dataset called TemplateGSM, which contains over 7 million grade school math problems and their solutions. This dataset, publicly available on Hugging Face, opens exciting new possibilities for training LLMs to reason mathematically. While this approach is promising, challenges remain. One concern is potential bias in the templates themselves, which could limit the kinds of problems LLMs learn to solve. Future work will focus on refining these templates, expanding to more advanced mathematical concepts, and addressing potential biases. The ultimate goal is to create AI systems that can truly understand and reason about the world, and unlocking mathematical reasoning is a crucial step in that journey.