Large Language Models (LLMs) are impressive, but they don't always learn effectively. Think about how humans learn best—we start with the basics, gradually tackling harder concepts. A new research paper explores this idea, introducing "Curriculum Demonstration Selection" (CDS), a method inspired by educational principles to make AI learning more efficient. Instead of throwing complex data at an LLM all at once, CDS feeds it information in stages, like a carefully planned lesson. This staged approach helps LLMs grasp the underlying patterns and reason more effectively, similar to how we learn math, starting with simple arithmetic before moving on to calculus. Researchers tested CDS on various tasks, including math problem-solving, common-sense reasoning, and even code generation. The results? CDS consistently boosted performance, especially on trickier problems where LLMs typically stumble. This suggests that a structured learning process can significantly enhance AI capabilities. While the research primarily focused on a few specific tasks and used pre-defined difficulty levels, future work could explore how to automatically assess complexity and apply CDS to a wider range of applications. This could lead to more robust and efficient LLMs that learn more like humans, progressively mastering complex tasks and ultimately pushing the boundaries of what AI can achieve.