Large language models (LLMs) are revolutionizing AI, but training them requires massive resources. Imagine trying to teach a computer everything it needs to know to understand and generate human language – it takes time, energy, and a lot of data. Now, researchers have developed a clever new technique called MultiTok, inspired by the way computers compress files, to make this process significantly faster and more efficient. MultiTok works by grouping common phrases into single “multi-word tokens.” Think of it like creating shorthand for the AI. Instead of processing each word individually, the model can learn entire phrases at once, like compressing a sentence into a single code. This significantly shrinks the training data size and speeds up the learning process. Experiments show that MultiTok can compress training data by up to 30% and speed up training time by 2.5x without sacrificing accuracy. This means LLMs can become smaller, faster, and cheaper to train, potentially bringing powerful AI capabilities to more devices and applications. While the research focuses on text classification tasks, the potential applications are broad. MultiTok’s innovative approach to tokenization could be a game-changer for other LLM tasks like text generation, translation, and even reasoning. This could lead to smaller, more powerful LLMs that run efficiently on everyday devices, opening up exciting possibilities for AI's future.