Reinforcement Learning from Human Feedback (RLHF)

RLHF

Definition

Reinforcement Learning from Human Feedback (RLHF) is a method for aligning AI models with human values by incorporating human judgments into the training process. It is often used to fine-tune large language models.

Purpose

The purpose is to make AI outputs safer, more useful, and aligned with human preferences. RLHF improves conversational systems by reducing harmful, biased, or irrelevant responses.

Importance

  • Provides human oversight in AI training.
  • Improves trustworthiness of AI systems.
  • Labor-intensive due to human annotation needs.
  • Related to preference modeling and alignment research.

How It Works

  1. Collect human feedback comparing model outputs.
  2. Train a reward model on human preferences.
  3. Use reinforcement learning to fine-tune the base model.
  4. Evaluate performance against alignment goals.
  5. Iterate with additional feedback.

Examples (Real World)

  • OpenAI ChatGPT: fine-tuned with RLHF for safer responses.
  • Anthropic’s Constitutional AI: guided by principles rather than direct feedback.
  • InstructGPT: early OpenAI model demonstrating RLHF.

References / Further Reading

  • Christiano et al. “Deep Reinforcement Learning from Human Preferences.” NeurIPS 2017.
  • OpenAI InstructGPT Paper.
  • NIST AI Risk Management Framework.