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

Impressive breakthroughs in math problem-solving! The combination of step-by-step reasoning and automated code verification has yielded remarkable accuracy gains. Excited to see how these models will be applied in AI research and development.