Large language models (LLMs) are impressive, but they sometimes stumble due to knowledge gaps. Think of it like a brilliant student who missed a few key classes – they have the potential, but lack specific pieces of information. Researchers are tackling this challenge with a clever new approach: diagnosing these knowledge gaps and filling them in without needing labeled data. This innovative technique, called "Label-free Curricular Meaningful Learning" (LaMer), works in three steps. First, LaMer identifies an LLM’s weaknesses by testing its responses to questions before and after providing relevant knowledge. If the knowledge significantly changes the LLM's answer, it highlights a gap. Next, LaMer creates tailored training examples based on the severity of the knowledge gap, much like a teacher crafting customized lessons. Finally, LaMer teaches the LLM this new knowledge progressively, starting with the simplest concepts and gradually increasing complexity, mirroring a well-structured curriculum. The results are impressive: LaMer consistently improves LLMs' performance across various reasoning and language understanding tests, often using significantly less training data than other methods. This label-free approach is not only more efficient but could pave the way for continuously evolving LLMs, adapting and learning from user interactions without constant human feedback. This advancement is a game-changer for AI, leading to more robust, reliable, and continuously improving language models.