Training massive AI models like ChatGPT gobbles up enormous amounts of data and computing power. But what if we could train these models faster and more efficiently with *less* data? New research suggests a surprising approach: data pruning based on information entropy. Think of it like decluttering your digital closet. You keep the most valuable, unique items and discard redundant or less informative ones. Researchers from Korea University applied this principle to language models. They ranked training data samples based on how much unique information they contained. Samples with low information content, like repetitive phrases or common knowledge, were pruned. The results were remarkable. Not only did this method save computing resources, it actually *improved* the performance of the language models on various tasks. The pruned models generalized better, achieving higher accuracy in text classification and textual similarity tasks compared to models trained on the full dataset. Why does less data lead to better performance? Researchers believe pruning redundant data helps prevent overfitting, where the model memorizes the training data instead of learning general principles. By focusing on the most informative samples, the model learns more efficiently and effectively. This research opens up exciting new possibilities for training powerful AI models with fewer resources. It challenges the conventional wisdom of "bigger is better" when it comes to datasets and suggests that a carefully curated, information-rich dataset can outperform a massive, unwieldy one. This could be a game-changer in the world of AI, paving the way for more sustainable and efficient training methods.