RLHF for GPT-3/InstructGPT-scale models: reward models, PPO fine-tuning, and human feedback

AI Impact Summary

RLHF combines pretraining a language model, training a reward model from human preferences, and fine-tuning the LM with reinforcement learning (PPO). It relies on human-annotated rankings rather than direct scalar scores, enabling more stable feedback signals and scalable alignment across large models (GPT-3, InstructGPT, Gopher, Chinchilla). The approach requires substantial compute and data-labeling infrastructure (including preference data generation and potential use of LoRA-style parameter freezing) and is sensitive to model size and reward-model capacity, impacting deployment timelines and ongoing costs. Implementing RLHF-enabled products will necessitate building end-to-end data pipelines for human feedback, reward modeling, and RL-based fine-tuning, which influences total cost of ownership and time-to-market for advanced language-model applications.