RLHF (RL from Human Feedback)

RLHF gives a learning system a practical answer to a hard question: what does “good” look like when nobody can write a complete reward function? Instead of trying to hand-code qualities such as helpfulness, harmlessness, clarity, or good judgment, people compare outputs and express preferences.

How the feedback becomes a reward
In reinforcement learning from human feedback (RLHF), human evaluators are shown alternatives—for example, two answers to the same prompt—and select the one they prefer. Those comparisons train a reward model: a model that predicts which response a person would rate more highly. An RL algorithm then adjusts the policy to produce outputs that score well under this learned reward. For language models, a common pipeline is:

Start with a pretrained model, then supervised fine-tune it on demonstrations of desirable behavior.
Collect human rankings of several candidate responses.
Train the reward model to assign higher scores to preferred responses.
Optimize the language-model policy against that score, commonly with PPO, while penalizing large departures from the original model.

Why this is reinforcement learning
The reward is not a correct answer label for every token. It is an evaluative signal about a whole generated response, and the model’s current behavior determines which responses are sampled, judged, and learned from. RL can therefore improve choices that are hard to specify directly: refusing a dangerous request gracefully, following nuanced instructions, or giving a concise answer rather than a technically correct but unusable one. The KL penalty used in PPO is important: without it, maximizing a learned reward can push the policy into strange, low-probability text that exploits weaknesses in the reward model.

What can go wrong
RLHF does not make the reward model equal human values; it makes it an imperfect predictor of collected judgments. A policy can reward hack by sounding confident, verbose, or agreeable because those traits fooled raters or the reward model. Feedback can also encode rater bias, miss rare safety cases, or fail when prompts shift beyond the comparison data. Strong RLHF systems therefore use diverse raters, adversarial evaluations, held-out tests, and constraints alongside reward optimization—not reward optimization alone.

RLHF (Reinforcement Learning from Human Feedback) trains a policy using a reward model learned from human judgments, such as rankings or preferences between outputs, rather than relying solely on a hand-designed reward. The policy is then optimized to maximize this learned reward. RLHF matters when desired behavior is difficult to specify directly: it converts qualitative human preferences into a training signal that can align learned actions with human expectations.

Imagine teaching a dog a trick: you do not explain every muscle movement. You show approval when it does something close to what you want, and over time it learns which choices earn that approval.

RLHF, short for Reinforcement Learning from Human Feedback, applies that idea to AI. Instead of relying only on rules written by programmers, people judge an AI’s responses—for example, choosing which of two answers is more helpful, safer, or polite. The AI then learns to favor responses that people tend to prefer. This matters because qualities like “helpful” or “respectful” are hard to capture perfectly in a checklist.