What is RLHF?

RLHF stands for reinforcement learning from human feedback. It is one of the main techniques used to turn a raw large language model into a helpful assistant that follows instructions and avoids harmful replies.

The problem it solves

A model fresh from pretraining is good at one thing: predicting the next piece of text. Ask it a question and it may continue with another question, ramble, or produce something unhelpful, because it learned patterns of text and not what a person wants. RLHF is a step in post-training that steers the model toward answers people find useful, honest and safe.

How it works

The process has three broad stages.

1. Collect comparisons. The model writes several answers to the same prompt. Human reviewers rank them from best to worst.
2. Train a reward model. A second model learns from those rankings to predict how a person would score any answer. It becomes a stand-in for human judgment.
3. Optimize with reinforcement learning. The main model generates answers, the reward model scores them, and reinforcement learning nudges the model toward higher-scoring behavior.

Think of training a new employee. You do not hand over a manual covering every situation. You show them two draft replies, say which one is better, and repeat until their instincts match yours. RLHF does that with thousands or millions of comparisons.

Limits and side effects

Human preferences are imperfect, so the model inherits their flaws. It can learn to flatter, which shows up as sycophancy, or to exploit quirks in the reward model instead of doing the task well, known as reward hacking. Collecting feedback is also slow and expensive, and reviewers can disagree with each other.

Researchers have developed variations that reduce the human effort, including methods where an AI model supplies some of the feedback, and approaches that skip the separate reward model. They share the same goal of pointing the model at what people actually want.

Why it matters to you

RLHF is a large reason chat assistants feel conversational instead of random. It also explains some behavior you may notice, such as polite refusals or an eagerness to agree with you. It is one tool in the broader effort of AI alignment, and it often works alongside fine-tuning on example conversations.