Large language models (LLMs) have revolutionized how we interact with technology, but training them is a resource-intensive process. Aligning these models with human values and preferences requires vast datasets and significant computational power. But what if we could achieve similar alignment results with far less data? New research explores how LLM alignment performance scales with data and introduces a novel method called Information Sampling for Alignment (ISA). This innovative approach suggests that, instead of using massive datasets, we can strategically select smaller, high-quality subsets of data to train LLMs effectively. The study found that alignment performance often plateaus after an initial rapid increase, indicating that more data doesn't necessarily translate to better alignment. ISA leverages information theory to identify these optimal data subsets, achieving comparable performance to full-dataset training while using less than 10% of the data. This breakthrough could lead to substantial savings in computational resources, energy consumption, and training time. Imagine aligning LLMs faster and more sustainably, making them more accessible for various applications. While this research focuses on a specific alignment algorithm (KTO), the findings pave the way for further exploration into efficient alignment techniques. Future research could investigate how these scaling laws apply to different LLMs, alignment algorithms, and data distributions. The potential for cost savings and faster development makes this a crucial area of study in the ongoing quest to create more powerful, safer, and ethical AI.