Large language models (LLMs) are impressive, but sometimes they miss the nuances of human communication. Think of those times an LLM gives two answers that are essentially the same, but one is marked as 'better' by the training data. This highlights a core problem: current AI training methods struggle with ties or subtle differences in quality. They're forced to choose a 'winner' even when there isn't one, leading to inefficient learning and a less accurate model. Researchers have developed a new technique called Tie-rank Oriented Direct Preference Optimization, or TODO, to address this limitation. TODO goes beyond the traditional 'like' or 'dislike' system by adding a 'tie' option. This seemingly simple change has profound implications. By recognizing ties, TODO avoids forcing the AI to make arbitrary distinctions and allows it to learn from subtle differences between responses. This leads to more nuanced preference modeling and ultimately, a better understanding of human intent. Experiments with Mistral-7B and Llama 3-8B models show that TODO outperforms existing methods, both when the training data includes ties and even when it doesn't. TODO's success hinges on two key improvements: more accurate handling of tied data and the ability to learn from the diverse information within tied responses. Imagine a human learning to write—they benefit from seeing multiple examples of good writing, even if those examples are similar. TODO gives LLMs the same advantage. This research doesn't just solve a technical problem; it moves us closer to truly human-like AI. By understanding ties and nuances, LLMs can communicate more naturally, follow instructions more accurately, and provide more useful responses. While current work focuses on direct preference optimization, TODO’s potential extends to other alignment techniques and even reward model training, opening exciting avenues for future research.