Reinforcement Learning from Human Feedback

Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that trains AI models using human-provided feedback, such as preferences or rankings, to guide the learning process. It combines reinforcement learning with human input to align model behavior with human values, often used to fine-tune large language models or other AI systems. This approach helps create more helpful, safe, and accurate AI by incorporating human judgment directly into the training loop.

Also known as: RLHF, Reinforcement Learning with Human Feedback, Human-in-the-loop Reinforcement Learning, Preference-based Reinforcement Learning, Human-aligned RL

Why learn Reinforcement Learning from Human Feedback?

Developers should learn RLHF when building AI systems that require alignment with human preferences, such as chatbots, content generators, or autonomous agents, to ensure outputs are ethical, relevant, and user-friendly. It is particularly crucial for applications in natural language processing, where models need to avoid harmful or biased responses, and in robotics, where human safety and intuitive interaction are priorities. RLHF enables more controlled and reliable AI deployment by reducing reliance on purely automated reward signals.

Learning Resources

• docs: OpenAI RLHF Documentation
• tutorial: Deep Reinforcement Learning from Human Preferences - Research Paper
• tutorial: RLHF Tutorial on Hugging Face
• course: Coursera: Reinforcement Learning Specialization
• video: YouTube: Introduction to RLHF by Two Minute Papers