Large language models (LLMs) like ChatGPT are revolutionizing how we interact with technology. But their training process, especially the crucial step of incorporating human feedback, presents a unique challenge: how do you ensure the feedback you're getting is genuine? Turns out, humans can be strategic, even in feedback scenarios. They might exaggerate their preferences to sway the model's learning in their favor. This intriguing problem has sparked a new area of research at the intersection of AI and game theory. Imagine a scenario where users rate LLM outputs. A user might give extreme scores (either very high or very low) to maximize their impact on the average rating, even if it doesn't accurately reflect their true preference. Traditional methods, like simply averaging feedback, are vulnerable to such strategic behavior and lead to suboptimal LLM performance. Researchers are now exploring smarter ways to aggregate feedback, moving beyond simple averages and towards dynamic weighting systems. These systems adjust the influence of each user's feedback based on its accuracy over time. The core idea is to give more weight to users who consistently provide feedback that aligns with the true preferences revealed through the model’s real-world performance. This approach encourages honesty by making strategic misreporting less effective. However, designing these systems isn't easy. Human preferences are complex and can change over time. The challenge lies in creating a system that can effectively track accuracy, adjust weights, and ultimately guide the LLM towards truly aligning with user needs, even when those users are playing a strategic game. This fascinating field is still in its early stages, but it holds immense promise for developing LLMs that are not just powerful but also truly reflective of genuine human preferences.