HomeResearch LibraryRLHF: Training Language Models to Follow Instructions w…
system architectureChapter 5NeurIPS 2022 · 2022

RLHF: Training Language Models to Follow Instructions with Human Feedback

Long Ouyang (OpenAI), Jeff Wu (OpenAI)

Abstract

We present InstructGPT, trained using reinforcement learning from human feedback to follow instructions. RLHF significantly improves alignment with human intent compared to supervised fine-tuning alone.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

RLHF is the foundational alignment technique that makes LLMs usable in enterprise settings. Without alignment to human preferences, even the most capable models produce outputs that are unreliable for enterprise use.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →RLHF methodology
  • →InstructGPT
  • →Human preference alignment

Topics

RLHFinstruction followingalignmenthuman feedback