The motivation behind InstructGPT is to create a model that can perform useful cognitive tasks, such as summarizing news articles or writing stories, by leveraging reinforcement learning with human feedback (RLHF). The team at OpenAI aims to fine-tune the model on an objective function that optimizes its performance as a useful assistant. They use human data, including labelers who provide preferences over generated outputs, to train the reward model and then optimize the neural network to produce good outputs according to this representation. The method has shown promising results, but there are challenges in scaling up to more powerful language models, such as evaluating their behavior and mitigating potential misalignment issues. Researchers are exploring new approaches, including scalable supervision and interpretability techniques, to address these challenges and ensure that the models align with human values.