Imagine teaching a computer to think like a programmer, meticulously crafting solutions to complex puzzles. That's the essence of CodePMP, a groundbreaking technique that uses the power of code to enhance the reasoning skills of large language models (LLMs). LLMs, the brains behind chatbots and AI assistants, often struggle with logic and math problems, much like a student facing a challenging exam. Traditional methods of improving LLM reasoning involve reinforcement learning from human feedback (RLHF), which can be expensive and time-consuming, like hiring a personal tutor for each AI. CodePMP offers a more efficient and scalable solution, similar to providing the AI with a comprehensive textbook of problem-solving strategies. The research cleverly uses code examples from public repositories like GitHub, creating millions of 'chosen' and 'rejected' code snippets, paired with descriptive prompts. These pairs serve as training data for the AI, teaching it to distinguish between correct and incorrect approaches. The model learns by identifying patterns and ranking strategies within the code, much like a student learns from solved examples. This 'code-driven' pretraining process, before fine-tuning on specific tasks, significantly improves sample efficiency and reduces reliance on manual annotation. It's like giving the AI a head start in its problem-solving education. Experimental results show that CodePMP significantly boosts LLM performance in both mathematical and logical reasoning tasks, outperforming traditional methods. It's like seeing a student's grades dramatically improve after adopting better study habits. CodePMP not only accelerates the learning process but also enhances the AI's ability to select the best solution from a set of alternatives, crucial for real-world problem-solving scenarios. This capability is like equipping the student with the critical thinking skills to choose the most effective solution. CodePMP's success highlights the potential of using alternative data sources, like code, to train more powerful and efficient AI systems. This research opens exciting new avenues for scaling up AI capabilities and pushing the boundaries of automated reasoning. While challenges remain, CodePMP presents a compelling vision of future AI, where the structure and logic of code unlock new levels of reasoning in language models.