Large language models (LLMs) have revolutionized how we interact with technology, but aligning them with human preferences remains a challenge. Imagine training a dog – you wouldn't rely on just one trainer's method, would you? Similarly, new research suggests that LLMs benefit from learning from multiple 'teachers,' or reference models. This innovative approach, called Multi-Reference Preference Optimization (MRPO), goes beyond traditional methods that rely on a single reference model. Instead, MRPO leverages the collective wisdom of several pre-trained LLMs, allowing the model being trained to learn from a wider range of perspectives. Think of it as a student learning from multiple experts, each with their own unique insights. The result? LLMs that are better aligned with human preferences, especially when training data is limited. This is particularly important in real-world scenarios where gathering extensive human feedback can be costly and time-consuming. MRPO has shown promising results in various language tasks, including math problem-solving and truthfulness evaluation. By incorporating the strengths of multiple reference models, MRPO improves the accuracy and reliability of LLMs, paving the way for more human-like AI interactions. While this research primarily focuses on smaller-scale models, it opens exciting possibilities for future development. Imagine the potential of training even more powerful LLMs with this multi-reference approach. As the open-source community continues to develop diverse LLMs, MRPO offers a powerful tool to harness their collective knowledge and create truly intelligent AI systems.