Large Language Models (LLMs) have shown amazing abilities, but math remains a challenge. Why? It requires precise, logical steps, a different kind of thinking than generating creative text. A new method called BEATS (BackVerify and Adaptive Disambiguate based Efficient Tree Search) aims to boost LLMs' math skills. How does it work? BEATS uses a clever three-pronged approach. First, it clarifies the question, ensuring the LLM isn't tripped up by confusing wording. Think of it as double-checking the problem before starting to solve it. Second, BEATS breaks down the problem into smaller, more manageable steps. Instead of trying to find the final answer in one go, it nudges the LLM to think step by step, making the process less overwhelming. Third, BEATS verifies its work. It uses a 'back-verification' technique where the LLM checks its own answers against the original problem, increasing the likelihood of getting the right result. This method significantly improves an LLM’s performance, especially when using models like Qwen2-7B-Instruct. In fact, BEATS helps Qwen2-7B-Instruct achieve over 60% accuracy on the challenging MATH benchmark, exceeding GPT-4's score. BEATS isn’t just about getting better at math problems. It's about enhancing how LLMs reason and solve complex issues, and step-by-step logic. It offers a new way to approach problem-solving in AI, opening doors for LLMs to tackle even more intricate challenges in the future. While promising, BEATS also highlights the ongoing need for efficient problem-solving in AI. Finding the right balance between accuracy and computational cost is key. Future research could refine verification methods and extend these techniques beyond math, potentially boosting LLMs’ overall reasoning abilities and tackling issues in science, engineering, and other logic-heavy fields.