Imagine a medical student tackling a complex diagnosis. They don't jump into advanced cases right away, do they? Instead, they start with the basics, building up their knowledge gradually. This intuitive approach—curriculum learning—is at the heart of new research exploring how to train large language models (LLMs) more efficiently in the medical field. A recent study from the University of Oxford investigated how human-inspired learning strategies can improve the training of LLMs for medical question answering. The researchers experimented with various methods, including 'blocked learning,' where questions are grouped by topic, and 'interleaved learning,' where questions are mixed from different categories. They tested these strategies across different LLMs, medical question-answering datasets, and even compared using human-defined vs. AI-defined question difficulty. What did they find? While human-inspired strategies did provide a modest accuracy boost (around 1%), the most effective method varied depending on the specific LLM and dataset. This lack of a one-size-fits-all solution highlights the complexity of adapting human learning to the realm of artificial intelligence. Interestingly, the researchers also found that having the AI itself define question difficulty proved more beneficial than relying on human-defined difficulty, opening new avenues for more automated and potentially cost-effective training. The use of AI-labeled data also performed similarly well. The implications of these findings are significant, particularly in medicine where high-quality data is often scarce and expensive. Fine-tuning LLMs with more efficient methods paves the way for building more accurate and data-efficient medical AI systems. This research, while revealing the limitations of directly translating human learning to machines, also highlights a key lesson: AI training needs to be strategic. Just like a medical student, LLMs benefit from structured learning experiences. Future research could delve deeper into personalized curricula for LLMs, perhaps even creating dynamic strategies that adapt as the model learns, pushing the boundaries of data-efficient training in medical AI and other fields.