Reinforcement learning with human feedback represents a novel approach to fine-tuning language models by incorporating human judgments and preferences into the training process. This paradigm involves training the model to generate text that maximizes a predefined reward signal, which is based on feedback provided by human annotators or evaluators. Through iterative cycles of text generation and human feedback, the model learns to align its output with human expectations and preferences, thereby enhancing its performance and adaptability across diverse linguistic contexts and tasks.
