---
title: "RLHF"
type: "glossary term"
url: "https://aifordevs.org/glossary#rlhf"
markdown: "https://aifordevs.org/glossary/rlhf.md"
publisher: "AI for Devs"
license: "https://aifordevs.org/reuse"
attribution: "Credit AI for Devs and link to https://aifordevs.org/glossary#rlhf"
also_known_as: "Reinforcement learning from human feedback"
---

# RLHF (Reinforcement learning from human feedback)

RLHF (reinforcement learning from human feedback) is training a model with a reward signal learned from human preference comparisons. It is one of the post-training steps that turns a pretrained model into an assistant that follows instructions.

People rank model outputs, a reward model learns those rankings, and reinforcement learning pushes the model toward outputs the reward model scores highly. Christiano et al. (2017) demonstrated learning from pairwise preferences on Atari and robot locomotion, and InstructGPT (Ouyang et al., 2022) applied it to language models.

## Example

In the InstructGPT paper, human evaluators preferred the output of a 1.3B-parameter model over that of the 175B-parameter GPT-3.

## Source

[Ouyang et al., Training language models to follow instructions with human feedback (2022)](https://arxiv.org/abs/2203.02155)

## Related terms

- [Fine-tuning](https://aifordevs.org/glossary#fine-tuning)

## Cite this term

AI for Devs. "RLHF." https://aifordevs.org/glossary#rlhf

- Page: https://aifordevs.org/glossary#rlhf
- Markdown: https://aifordevs.org/glossary/rlhf.md
- Reuse: Quote or summarize with a link to this page. Terms: https://aifordevs.org/reuse
- Publisher: AI for Devs