Large Language Models (LLMs) have taken the world by storm, demonstrating impressive abilities in writing, coding, and even creative tasks. But when it comes to complex reasoning, especially math, they often stumble. Imagine trying to solve a multi-step math problem, but you can't quite decide which step to take next. That's the challenge LLMs face. New research introduces MindStar (M*), a clever framework that helps LLMs become much better mathematical thinkers. Instead of retraining these massive models, which is expensive and time-consuming, MindStar acts like a guide at inference time, helping the LLM choose the right reasoning path. It works by turning the reasoning process into a search problem, exploring different possible steps and using a 'reward model' to assess how promising each step is. This reward model acts like a built-in tutor, giving the LLM feedback on its thinking process. The results are impressive. When applied to open-source models like Llama-2 and Mistral, MindStar significantly boosts their math performance, rivaling even giants like GPT-3.5, but with a fraction of the size and computational cost. This is a big deal because it opens doors to making powerful AI reasoning more accessible and sustainable. While MindStar requires some extra computation during inference, the gains in accuracy, especially on challenging math problems, make it a worthwhile trade-off. This research points to a promising future where we can make AI smarter not just by making them bigger, but by teaching them how to think more effectively.