Reward Model

Reward models are crucial for aligning large language models (LLMs) and other AI systems with human preferences, enabling more helpful and harmless behavior. Current research focuses on improving reward model accuracy and robustness, exploring techniques like preference optimization, multimodal approaches incorporating both text and image data, and methods to mitigate biases and noise in reward signals, often employing transformer-based architectures and reinforcement learning algorithms. These advancements are vital for building more reliable and trustworthy AI systems, impacting both the development of safer LLMs and the broader field of human-centered AI.