What is Reinforcement Learning from Human Feedback?
Also known as: RLHF
Reinforcement Learning from Human Feedback (RLHF) is a training technique that improves AI models by using human preferences to guide the learning process instead of relying only on fixed rewards.
RLHF works in three main stages. First, humans rank or rate different AI outputs for the same prompt. Second, these rankings train a separate reward model that predicts how much humans would like a new output. Third, reinforcement learning uses this reward model to adjust the original AI so it produces higher-scoring responses.
The key idea is to translate subjective human values such as helpfulness, honesty, and safety into a signal the model can optimize. This allows the AI to learn behaviors that are hard to specify with simple rules or labeled data alone.
Common algorithms used in the final stage include Proximal Policy Optimization (PPO), which keeps updates stable while maximizing the learned reward.
Example
After an initial version of ChatGPT generates several possible answers to a question, human raters pick the most helpful and harmless one. These choices train a reward model that later guides further training so the chatbot produces better answers on its own.
Why it matters
RLHF is currently the main method used to align large language models with human expectations, making systems like ChatGPT and Claude more useful and less likely to produce harmful content.
Frequently asked questions
Supervised learning trains on fixed correct answers, while RLHF uses human rankings to create a flexible reward signal that teaches the model what people prefer even when no single correct answer exists.
Related terms
Reinforcement Learning (RL) is a machine learning method where an agent learns to make sequential decisions by interacting with an environment, receiving rewards or penalties, and aiming to maximize its long-term reward.
Adam (Adaptive Moment Estimation) is a popular optimization algorithm used to train machine learning models by iteratively updating parameters based on gradients.
Classification is a supervised machine learning task that assigns input data to one of several predefined categories or classes based on patterns learned from labeled training examples.
Clustering is an unsupervised machine learning technique that automatically groups similar data points together into clusters based on their features, without using any labeled examples.
Gradient descent is an optimization algorithm that finds the minimum of a function by repeatedly moving in the direction of the steepest downward slope. In machine learning it is used to minimize a model's error by adjusting parameters step by step.
A hyperparameter is a value or setting chosen by the user before training a machine learning model that controls the learning process itself.