The work
What is RLHF? The work behind it, explained for AI trainers
Updated · By the AI Training Pay team · How we research
RLHF, reinforcement learning from human feedback, is how AI labs turn a model that can write into a model that writes what people actually want. The "human feedback" is the work AI trainers are paid for. Here's how it works in plain English, and what each task type you'll see in listings actually involves.
RLHF in three steps
- Show it good answers. People write ideal responses to prompts, and the model is fine-tuned on them. This is supervised fine-tuning (SFT).
- Ask people which answer is better. The model writes several answers to the same prompt. Trainers rank them. Those rankings train a separate "reward model" that learns to predict what a person would prefer.
- Optimize against the judge. The main model is trained, with reinforcement learning, to produce answers the reward model scores highly.
OpenAI described this recipe in its 2022 InstructGPT paper, and Anthropic published a similar approach for training helpful and harmless assistants the same year. Newer methods such as direct preference optimization (DPO) skip the separate reward model but still rely on human preference data.
Sources: Ouyang et al., Training language models to follow instructions with human feedback (2022), Bai et al., Training a helpful and harmless assistant with RLHF (2022), Rafailov et al., Direct Preference Optimization (2023)
Why labs pay experts
A model can only learn the standard its judges hold it to. A generalist can tell which answer reads better; only a cardiologist can tell which dosing advice is right. As models got good at everyday questions, the feedback that still improves them moved to specialist territory. That's why medicine, law and finance roles top the Pay Index.
The task types you'll see
| Task | What you do | Why it matters |
|---|---|---|
| Preference ranking | Read two or more answers to the same prompt and pick the better one, often with a strength rating and a written reason. | Feeds the reward model directly. |
| Rubric grading | Score one answer against a checklist: accurate, complete, safe, follows the instructions, right format. | Common for expert work, because rubrics can encode what a specialist checks for. |
| Writing ideal answers | Write the response you'd want the model to give. Also called demonstrations or SFT data. | Teaches the model the target before preference training. |
| Prompt writing | Write hard, realistic questions in your field, sometimes ones the current model gets wrong. | Builds evaluation sets and training prompts. |
| Red teaming | Try to get the model to produce something harmful, false or against its rules, and document how. | Finds safety gaps before release. |
| Fact checking | Verify every claim in an answer against sources and flag errors. | Catches confident hallucinations. |
| Code review | Run, test and fix model-written code, or judge which solution is correct and cleaner. | The largest technical category. |
| Audio and multimodal | Record speech, transcribe, or judge answers about images and audio. | Trains voice and vision features. |
Doing the work well
- Accuracy beats style. A well-written wrong answer should lose to a plain right one. Graders check whether you noticed.
- Follow the project's definitions. "Helpful" and "harmless" mean whatever the guidelines say they mean on that project.
- Write reasons someone else can check. "B cites the 2024 guideline; A uses the superseded dose" is useful. "B is better" isn't.
- Consistency counts. Reviewers compare your ratings with other trainers'. Being reliably careful matters more than being fast.
New to the vocabulary? The AI training glossary covers SFT, reward models, rubrics, red teaming and the platform slang you'll hear. Ready to start? How to become an AI trainer.
Questions people ask
What does RLHF stand for?
Reinforcement learning from human feedback: a way of training AI models using people's judgments about which answers are better.
Is RLHF the same as data annotation?
No. Data annotation is the broader category of labeling data, like tagging images or transcribing audio. RLHF work is the part where people judge and write model answers, and it pays more because it needs judgment and often expertise.
Do AI trainers need to understand RLHF?
Not in technical depth. Knowing why your ranking or rubric score matters helps you do the work well: the model learns exactly what you reward, including your mistakes.
Is RLHF still used in 2026?
Human feedback is still central, though methods have multiplied. Techniques such as direct preference optimization use the same human comparison data without a separate reward model, and labs increasingly use expert-written rubrics and verifiable answers alongside rankings.