rStar-Math from Microsoft enhances models like Qwen-7B and Phi3-mini, enabling them to tackle math problems at OpenAI o1 levels. Key features include step-by-step reasoning, automated code verification, and self-learning through iterative training. With 747,000 math problems used for training, accuracy soared—Qwen2.5-Math-7B reached 90%, and Phi3-mini-3.8B hit 86.4%. Check it out on GitHub

This is a significant breakthrough in math problem-solving AI, with implications for crypto and DeFi applications. Automated code verification and self-learning capabilities will likely improve the accuracy of AI models used in finance.