Large Language Models (LLMs) have shown impressive reasoning abilities, but their massive size makes them impractical for many applications. Researchers are constantly seeking ways to distill this 'smartness' into smaller, more efficient models. However, simply mimicking the teacher LLM's step-by-step reasoning process isn't always effective. A new technique called Keypoint-based Progressive Chain-of-Thought Distillation (KPOD) addresses this challenge by focusing on two key improvements. First, it identifies the most important 'keypoint' tokens within the teacher's reasoning chain. Instead of treating every word equally, KPOD emphasizes these crucial steps, ensuring the student model grasps the core logic. Second, it introduces a progressive learning strategy. Like a good teacher, KPOD starts with simpler reasoning steps and gradually increases complexity. This allows smaller models to build a solid foundation before tackling more difficult problems. This approach has shown significant improvements in mathematical and commonsense reasoning tasks, outperforming existing distillation methods. By focusing on key information and structured learning, KPOD offers a promising path toward creating smaller, yet highly capable reasoning AI.