Ever wondered what happens inside a large language model (LLM) as it learns? A fascinating new study from EleutherAI has explored the inner workings of LLMs during training. Researchers looked at how these models develop "circuits," which are like computational pathways responsible for specific skills, such as understanding grammar or reasoning. What they found was surprising: These circuits are remarkably stable across massive amounts of training data and even between models of different sizes. This means the way an LLM solves a problem remains largely consistent, even if the specific parts it uses shift around a bit. This research challenges previous assumptions by showing that the internal mechanisms of LLMs are more consistent than we thought. This has big implications for how we study and interpret these powerful AI models. By examining these circuits, we can gain a deeper understanding of how LLMs learn and perform complex tasks, paving the way for more robust and interpretable AI systems in the future. This discovery also suggests that we might be able to glean valuable insights from smaller, more manageable LLMs that still hold true for their larger counterparts, significantly reducing the computational costs associated with AI research.