Imagine asking an AI for restaurant recommendations. You'd hope it reflects the actual preferences of diners, right? But what if the AI, despite being trained on real human feedback, starts heavily favoring popular choices, even if a significant portion of people prefer something else? This isn't just hypothetical; it's a real problem stemming from a hidden bias in how we train AI, specifically large language models (LLMs) like ChatGPT. The most common training method, called RLHF (Reinforcement Learning from Human Feedback), uses a reward model to learn what humans like. However, a new research paper reveals a critical flaw: RLHF's algorithm has a built-in bias due to how it uses a 'reference model' during training. This reference model, often a pre-trained LLM, can unintentionally pass its own biases onto the AI being trained. In extreme cases, this can lead to 'preference collapse,' where the AI completely ignores minority preferences. Think about the restaurant example again: if the reference model slightly favors burgers, the trained AI might overwhelmingly recommend burger joints, even if many people prefer sushi or tacos. To fix this, researchers have developed a new method called Preference Matching RLHF (PM RLHF). This approach ensures the AI accurately reflects the full spectrum of human preferences, not just the dominant ones. It works by adding a special 'regularizer' to the training process, encouraging the AI to balance popular choices with less common ones. Early tests of PM RLHF on LLMs like OPT-1.3B and Llama-2-7B show promising results, improving preference alignment by up to 41%. This means AI trained with PM RLHF is better at capturing the true diversity of human preferences, leading to fairer and more useful recommendations, whether it's for restaurants, products, or even information.