Large Language Models (LLMs) have wowed us with their language abilities, but math? Not so much. They can write poems and summarize articles, but solving multi-step math problems often trips them up. Why? It turns out that mathematical reasoning requires a different kind of thinking than generating human-like text. It's not enough to understand the words; LLMs need to grasp the underlying logical structure and manipulate symbolic concepts. New research explores a clever way to boost LLMs' mathematical prowess through specialized training techniques. Researchers are experimenting with 'question paraphrasing,' using another powerful LLM (GPT-4) to reword math problems in different ways. This helps the LLM being trained generalize better and avoid getting stuck on specific phrasing. Think of it like learning different ways to ask the same question—it broadens your understanding. But that’s not all. They’re also using innovative training objectives. One, called 'Rationale Re-Ranking,' shuffles the steps involved in solving a math problem and trains the LLM to put them back in the correct order. This helps the LLM learn the logical flow of mathematical reasoning. Another objective, 'Mistake Identification,' intentionally introduces errors into the reasoning process and trains the LLM to spot and correct them. This builds resilience and helps prevent the cascading errors that often plague LLMs in multi-step problems. The results are promising. Across several benchmark datasets, these specialized training methods significantly improve LLMs' accuracy on math problems, especially those requiring multiple steps. While the improvement on simpler problems is less dramatic, the real gains appear when the reasoning gets complex. However, challenges remain. LLMs still struggle with exceptionally long reasoning chains, and potential biases in training data can influence performance. The research also highlights the importance of all training components working together; removing any single piece reduces overall effectiveness. While not a perfect solution, this research offers a significant step towards LLMs that can truly reason mathematically. It opens doors to a future where LLMs can tackle more complex real-world problems involving intricate mathematical reasoning, from scientific research to financial modeling.