How does reinforcement learning from human feedback (RLHF) integrate into ChatGPT architecture pipelines?

RLHF aligns ChatGPT outputs with human preferences using reward models trained on human feedback.

RLHF is a multi-stage training pipeline used to align ChatGPT behavior with human expectations. First, a base model is pretrained on large text corpora. Then supervised fine-tuning (SFT) is applied using curated human-written responses.

Next, a reward model is trained to score outputs based on human preference rankings. Finally, reinforcement learning (often PPO) is used to optimize the model to maximize reward signals.

This architecture improves helpfulness, safety, and alignment but introduces complexity in training stability and reward hacking risks.