RLHF capability: integrating human feedback into language model training (GPT-3, InstructGPT, PPO-based fine-tuning)

AI Impact Summary

This article outlines the RLHF training pipeline: pretrain a language model, build a reward model from human preferences, and fine-tune the LM with reinforcement learning (PPO). It anchors the approach with examples from GPT-3 and InstructGPT, and references large-scale models from Anthropic and DeepMind (Gopher, Sparrow, Chinchilla) to illustrate data and compute implications. For a technical team, the takeaway is that effective RLHF hinges on data generation/annotation, reward-model calibration, and careful parameter management (what to freeze vs. fine-tune) to balance cost with alignment benefits.