RLHF vs DPO vs RLAIF: What Human Raters Do

The short answer

RLHF and DPO both need the same raw material from people: judgments of which response is better. RLHF turns those judgments into a reward model and then runs reinforcement learning; DPO trains on the pairs directly. RLAIF swaps most of the human labels for a model's, so the people you pay write principles, build test sets and audit the machine's labels instead.

On this page

The three acronyms describe training methods, but a data budget is spent on people, so the useful question is narrower: for each method, what does a rater actually sit down and do, and how long does it take?

What does each method need from people?

RLHF (reinforcement learning from human feedback) is the pipeline OpenAI described in its 2022 InstructGPT paper. It has three stages. People write demonstration answers that the model is first fine-tuned on. People then compare several model outputs for the same prompt and rank them. Those rankings train a reward model, and the language model is tuned with reinforcement learning to score well against it.

DPO (direct preference optimization), introduced by Rafailov and colleagues in 2023, removes the reward model and the reinforcement learning loop. It trains the model straight from pairs of a chosen and a rejected response. The engineering is simpler and the training cheaper to run, but the human input is the same kind of data: pairwise preferences.

RLAIF (reinforcement learning from AI feedback) replaces most human preference labels with labels from a model. In Anthropic’s Constitutional AI work, a model critiques and revises responses against a written list of principles, and AI-generated comparisons train the preference model for harmlessness. A 2023 Google study by Lee and colleagues reported RLAIF results comparable to RLHF on the summarization and dialogue tasks it tested.

Where does the human time go?

What people produce for each method (summary of the papers cited below)
Method	What raters produce	Reward model	Main human cost
RLHF	Demonstrations, then rankings or pairwise choices	Yes, trained on the human rankings	Volume of comparisons, plus expert time on hard domains
DPO	Pairwise choices (chosen and rejected)	No	Same comparisons as RLHF; no saving on the human side
RLAIF	Principles, evaluation sets, audits of AI labels	Usually, trained on AI labels	Fewer, more senior hours: writing rules and checking the judge

The point teams miss most often is the DPO row. Switching from RLHF to DPO changes your training code and your compute bill. It does not change what you buy from a data vendor, because both methods consume the same comparisons. If a vendor quotes differently for “RLHF data” and “DPO data” on the same task, ask what is actually different in the deliverable.

What does a comparison task look like in practice?

A rater sees a prompt and two (sometimes more) responses. They read both, decide which is better against the rubric, and often add a strength score (“slightly better”, “much better”) and a short written reason. Some projects let raters mark a tie, and some ask them to rewrite the losing answer so it becomes a demonstration too.

Time per task is the number that matters. A short chat turn judged on helpfulness can take a few minutes. A long code diff that has to be run, or a multi-step agent trace checked against tool outputs, can take an hour and needs someone who could have written the answer. That is why expert comparisons cost many times more than generalist ones, whichever method consumes them.

What do you pay for in an RLAIF project?

RLAIF moves human effort upstream and downstream of the labels.

Upstream: the principles

Someone has to write the constitution or rubric the AI judge follows, and that someone should know the domain. Vague principles produce confident, consistent and wrong labels at scale, which is worse than noisy human labels because nothing flags them.

Downstream: checking the judge

You still need a human-labeled set to measure how often the AI judge agrees with people, broken down by criterion. You need audits of samples from production, and you need people for the cases the judge handles badly: safety edge cases, specialist facts, cultural context. The hours are fewer than in a full RLHF program, but each one tends to be more senior.

Which should you brief a vendor for?

Brief for the deliverable, not the acronym. Tell the vendor:

The format: pairs, rankings of more than two, per-criterion scores, demonstrations, or a mix.
Who must judge: generalists, domain experts or specialists who can verify the answer.
Volume for a pilot and for production, and the time you expect per task.
If you plan RLAIF, the size of the human calibration set and the audit rate you want.
The quality figure you will hold them to, such as agreement on shared items or accuracy on your gold tasks.

Our pricing models page covers how vendors charge for each of these, and the vendor table on the RLHF companies home page shows what each says it supplies.

Common mistakes when choosing between them

Buying demonstrations when you need judgments. Writing an ideal answer takes far longer than picking the better of two. If your model already writes reasonable drafts, comparisons are usually the cheaper signal.
Assuming RLAIF means no human budget. It means a smaller one, spent on the people who are hardest to hire.
Ignoring disagreement. On close calls, raters will split. That spread is information about the task, and a vendor who reports only the majority label throws it away.

Pick the method for your training stack; pick the vendor for the judgments your raters will have to make.

Sources for this post

RLHF vs DPO vs RLAIF: what are you paying people to do?