Imagine training an AI to write code, feeding it massive amounts of data from the internet. Seems like the more data, the better, right? Not always. Recent research reveals a surprising secret: sometimes, less is more. In the world of code, 'spaghetti code'—long, tangled files—can actually hinder an AI's learning. Think of it like trying to learn a recipe from a cookbook filled with redundant instructions and unnecessary steps. Researchers discovered that these overly long files, often filled with repetitive or low-quality code, take up a significant chunk of training data without contributing much value. So, they experimented with a simple yet powerful technique: pruning, or removing, the longest files from the training dataset. The results were impressive. By cutting out the 'spaghetti,' they achieved a two-fold increase in training efficiency, meaning the AI learned just as effectively with only half the data. Even more remarkably, this pruning method led to a significant boost in the AI's performance on coding tasks, especially in early training stages. This suggests that focusing on higher-quality, concise code samples can significantly accelerate AI learning. However, there's a catch. While pruning long files improves performance on shorter coding tasks, it can sometimes lead to overfitting. This means the AI becomes really good at specific types of short code but struggles with longer, more complex programs. This highlights an important consideration: evaluating AI models on a diverse range of code samples. Focusing solely on short code snippets might give a misleading impression of the AI's true capabilities. The research also raises interesting questions about how we find and curate high-quality data for training AI, especially as models evolve to handle longer and more intricate code. One promising approach is to combine related code files into longer, more meaningful training sequences, simulating real-world coding scenarios. In conclusion, this research suggests that less can indeed be more when training code-generating AI. By strategically pruning long, low-quality code files, we can not only boost efficiency but also improve performance, paving the way for smarter and more capable AI coding assistants.