RLHF
Reinforcement Learning from Human Feedback (RLHF) is a technique that fine-tunes AI models by using human judgments to guide their behavior. It trains a reward model based on human preferences, then uses this model to further train the AI through reinforcement learning.
Why it matters
RLHF is important for aligning AI models with human values and intentions, making them more helpful, honest, and harmless. This benefits engineers and operators building AI-powered applications by creating more predictable and reliable AI systems.
How it works
The process involves humans rating or ranking different outputs from an AI model. These ratings form a dataset used to train a separate reward model, which learns to predict human preferences. The original AI model is then updated using reinforcement learning to maximize the score predicted by the reward model, thus aligning its outputs with the learned human preferences.
Auto-generated from Kapyn's news stream · updated Jun 15, 2026