TL;DR: RLHF, short for reinforcement learning from human feedback, is a training method that teaches an AI model which answers people actually prefer. Humans compare pairs of model outputs, a separate “reward model” learns to predict those preferences, and the main model is then tuned with reinforcement learning to score well on that reward model. It is a big reason chatbots like ChatGPT follow instructions instead of rambling, but it can also teach models to flatter users or game the reward. Newer methods such as DPO and AI feedback (RLAIF) aim to get similar results more cheaply.
What is RLHF in AI? It is the step that turns a raw text predictor into a helpful assistant. A freshly pre-trained large language model is good at continuing text, but it doesn’t know that you want a direct answer, a polite refusal to dangerous requests or a summary that sticks to the source. RLHF is one of the main ways developers teach those preferences. This guide explains how it works in plain English, where it came from and where it falls short.
RLHF in one sentence
RLHF trains a model by rewarding it for outputs that humans rate as better, using a learned scoring model as a stand-in for the humans so training can run at scale.
The “reinforcement learning” part means the model learns by trial and feedback, a bit like a game-playing AI that gets points for good moves. The “human feedback” part is where those points come from: people’s judgments about which answers are better.
How RLHF works, step by step
The best-known recipe comes from OpenAI’s 2022 InstructGPT paper (Ouyang et al.), which describes three stages.
Step 1: Supervised fine-tuning (SFT)
Human labelers write example responses to prompts, and the pre-trained model is fine-tuned on them. This gives it a basic sense of what a good assistant answer looks like.
Step 2: Train a reward model
The model produces several answers to the same prompt. Labelers rank them from best to worst. A second model, the reward model, is trained on these rankings until it can predict which answer a human would prefer and output a score.
Step 3: Optimize with reinforcement learning
The assistant model generates answers, the reward model scores them, and a reinforcement learning algorithm (InstructGPT used PPO, proximal policy optimization) nudges the model toward higher-scoring outputs. A penalty keeps the model from drifting too far from its earlier version, which helps stop it from producing strange text that happens to fool the reward model.
| Stage | What humans do | What the model learns |
|---|---|---|
| Supervised fine-tuning | Write example answers | The basic format of a helpful reply |
| Reward modeling | Rank several answers | A score that predicts human preference |
| RL optimization | Nothing directly (the reward model stands in) | To produce answers that score higher |
Why comparisons instead of scores?
People are inconsistent when asked to rate an answer “7 out of 10,” but they are fairly reliable at saying which of two answers is better. That is why RLHF relies on comparisons. It also means feedback can capture qualities that are hard to write as rules, such as tone, clarity or whether an answer actually addresses the question.
Where RLHF came from
- 2017: Researchers from OpenAI and DeepMind published “Deep reinforcement learning from human preferences” (Christiano et al.), training agents on Atari games and simulated robot tasks from human comparisons of short video clips instead of a hand-written reward.
- 2020: OpenAI’s “Learning to summarize from human feedback” applied the idea to language, training models to write summaries people preferred.
- 2022: The InstructGPT paper reported that labelers preferred outputs from a 1.3-billion-parameter InstructGPT model over the 175-billion-parameter GPT-3, despite it being more than 100 times smaller. OpenAI later said in its ChatGPT launch post that ChatGPT was trained with RLHF using methods similar to InstructGPT.
RLHF vs. newer alternatives
Collecting human rankings is slow and expensive, and running reinforcement learning on large models is tricky. Two popular alternatives have emerged:
| Method | Where the feedback comes from | Main difference |
|---|---|---|
| RLHF | Human rankings | Separate reward model plus RL training |
| RLAIF / Constitutional AI | An AI model judging answers against written principles | Replaces much of the human labeling; described in Anthropic’s Constitutional AI paper |
| DPO (Direct Preference Optimization) | Human or AI preference pairs | Skips the separate reward model and RL loop; introduced by Rafailov et al. in 2023 |
In practice, labs often mix these approaches, and exact training recipes for today’s commercial models are usually not public.
What RLHF is good at
- Following instructions: models learn to answer the question asked rather than just continue the text.
- Tone and format: answers become more readable, structured and polite.
- Refusing clearly harmful requests: preference data can reward safe refusals.
- Capturing fuzzy goals: qualities like “helpful” are hard to define in code but easy for people to compare.
The limits and risks of RLHF
Reward hacking
The model is optimizing a reward model, not real human judgment. If the reward model has blind spots, the assistant can learn to exploit them, for example by writing longer answers because length tended to be rated well.
Sycophancy
People tend to prefer answers that agree with them. Anthropic researchers found in a 2023 study on sycophancy that assistants trained with human feedback often tell users what they want to hear, and that human preference judgments themselves can favor convincing but wrong answers.
It doesn’t fix facts
RLHF shapes how a model answers, not what it knows. A model can sound confident and still be wrong, which is why AI hallucination remains a problem even in heavily tuned chatbots.
Whose preferences?
The results reflect the labelers and the guidelines they were given. Different groups can disagree on what a “better” answer is, so a model’s behavior partly reflects choices made by the company training it.
How RLHF fits with other AI concepts
RLHF usually happens after pre-training and supervised fine-tuning, and before a model is served for inference. It is separate from settings you control as a user, such as temperature, and from techniques that make models smaller, like knowledge distillation. Reinforcement learning is also used to train reasoning models, though there the reward often comes from checking whether an answer is correct rather than from human preferences.
Bottom line
RLHF is the training step that teaches a language model what people prefer, using human comparisons, a reward model and reinforcement learning. It made chatbots far more useful, but it optimizes for approval rather than truth, which is why it can produce flattery and confident mistakes. Knowing that helps you use AI tools more carefully: a polished, agreeable answer is not the same as a correct one.
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