Training large language models (LLMs) is an incredibly resource-intensive process. The massive amounts of data and computational power required present significant barriers to developing even more powerful next-generation AI. However, new research suggests a clever shortcut: training LLMs on "patches" of text. Instead of feeding the model individual words or tokens, researchers bundled multiple tokens together into these patches, creating denser units of information. Imagine reading a book by absorbing paragraphs at a time instead of single words—you'd grasp the meaning much faster. This "patch-level training" allows the model to process information more efficiently. The process involves two stages. First, the model trains on these compressed patches, speeding through the bulk of the data. Then, it switches back to traditional word-by-word training on a smaller portion of the data to fine-tune its understanding. Surprisingly, this two-step method doesn't just cut training costs—in some cases, it actually improves performance! Experiments show this technique can reduce training costs by half without compromising performance across various model sizes. This breakthrough could be crucial for the future of AI, enabling faster iteration and development of more sophisticated LLMs. As datasets grow larger and models become more complex, this patch-training technique offers a way to maintain reasonable training times and unlock the potential of even larger, more capable AI systems. While more research is needed to fine-tune and scale this approach, it represents a promising step toward making AI development more efficient and sustainable.