Imagine teaching a dog a new trick. You show it exactly what to do, reward it when it gets close, and correct it when it's wrong. Seems straightforward, right? But what if, after painstakingly teaching it to fetch a ball, it suddenly starts bringing you slippers, socks, and anything remotely round? That's kind of what's happening with fine-tuning large language models (LLMs). Researchers are finding that while fine-tuning appears to teach models new skills, the underlying *logic* of their learning is flawed. A new study delves into this problem by examining "empirical influence functions." Essentially, they look at how individual training examples change the model's behavior. Ideally, a model should learn more from high-quality, relevant data and less from noisy or irrelevant data. It should also understand logical relationships, like if A implies B and B implies C, then A implies C. Unfortunately, the research reveals that popular models often violate these basic principles. They learn equally from good and bad data, struggle with logical chains, and often overgeneralize. So, while fine-tuning can improve performance on specific tasks, it doesn't necessarily lead to true understanding. The study also highlights a surprising finding: providing information in the prompt, rather than through fine-tuning, often leads to better reasoning. This suggests that LLMs are better at using information in context than integrating it into their learned knowledge. This research raises important questions about the limitations of current AI training methods. It suggests that simply feeding models more data isn't enough. We need to develop new techniques that encourage them to learn and reason more like humans. The future of AI depends on it.