RLHF (Reinforcement Learning from Human Feedback)

What is RLHF?

RLHF, or Reinforcement Learning from Human Feedback, is a training technique used to make AI models more helpful, harmless, and honest by incorporating direct human judgment into the learning process. Think of it like training a new employee: instead of just giving them a textbook, you have experienced colleagues review their work and say "this response was great" or "that one missed the mark." Over time, the employee learns to produce work that matches what reviewers consider high quality.

How Does It Work?

RLHF typically follows three stages. First, a base language model is pre-trained on large amounts of text data. Second, human evaluators compare pairs of model outputs and indicate which response they prefer. These preferences are used to train a separate reward model that can predict how a human would rate any given output. Third, the original language model is fine-tuned using reinforcement learning (specifically an algorithm called PPO) to maximize the score from the reward model. The result is a model that generates responses more aligned with human expectations.

Why Does It Matter?

RLHF is the key ingredient behind the leap from raw language models to polished AI assistants like ChatGPT and Claude. Without it, models tend to produce outputs that are technically fluent but may be unhelpful, evasive, or even harmful. RLHF bridges the gap between "can generate text" and "generates text that humans actually find useful and safe." It is a cornerstone of the broader AI alignment field, which seeks to ensure powerful AI systems behave according to human values and intentions.