Large language models (LLMs) are getting better at handling long pieces of text, which is great for tasks like summarizing lengthy articles or writing code. But there’s a catch: the bigger the context window of an LLM, the more memory and processing power it needs, which makes it more expensive and slower. Current methods for dealing with this try to be selective about which parts of the text the model remembers, but sometimes they throw away important bits of information that are needed later. Researchers have found a clever way to shrink the LLM's memory footprint *without* losing those crucial details. The key insight is that words closer to the current point in the text are generally more important than words further away. This allows the model to prioritize what it remembers. By cleverly sharing information about less important words between the different layers of the model, they can cut down the memory use by a significant amount. This new technique, called POD, has been shown to reduce memory usage by 35% without sacrificing performance on tasks like question answering, summarization, and even code generation. This is a promising step towards making LLMs more efficient and practical for handling truly long texts. The ability to work with longer contexts opens up exciting possibilities for more complex and nuanced tasks, making AI even more powerful in the future.