RLHF preference data. Judged by people who know the answer.
A reward model learns whatever its raters reward. On a hard prompt, a generalist picks the fluent answer; a specialist picks the correct one. eeko routes every comparison to credential-checked raters in the prompt's own field, captures a rationale with each label, and reports agreement with each batch.
What is RLHF preference data?
RLHF preference data is the record of a human choosing between model outputs: which of two completions is better, how several completions rank, or how one completion scores against a rubric. Reward models are trained on those choices, and policies are then optimized against the reward model (or, with DPO, against the pairs directly). Every bias in the raters becomes a bias in the model, amplified by optimization.
The bias that matters most is the preference for confidence. A rater who cannot check a proof, a dosage, or a clause will favor the longer, better-formatted, more assured answer. The reward model learns that assurance is good, and the policy learns to sound certain whether or not it is right. The fix is not a better rubric. It is a rater who can tell.
eeko tunes the models inside the enterprise AI agents we deploy, so we collect preference data the way we would want to receive it: physicians on clinical prompts, practising attorneys on legal reasoning, mathematicians on proofs, working engineers on code and system design, with every judgment explained and every batch measured.
Human feedback data, in the shapes trainers expect.
Pairwise, listwise, or absolute. One rubric, one rater pool, one delivery schema, whichever shape your reward model or DPO pipeline consumes.
- Preference plus strength
- Rater ID stored on every pair
- Three to eight completions per prompt
- Partial orders where items are equivalent
- Dimensions and scales agreed in writing
- Calibration set run before production
- Structured text beside the label
- Usable for critique and judge models
- Overlap share set in the spec
- Chance-corrected statistics by label type
- Decision and reason recorded per item
- Rubric feedback loop
The confident wrong answer is the one that matters.
Capable models rarely fail by sounding unsure. They fail by being wrong and sounding right. A rater who cannot verify the content cannot catch that failure, and a reward model trained on their labels learns to prefer it.
Who rates what
- Clinical prompts — physicians and pharmacologists, with the most sensitive categories restricted to those with the relevant specialty.
- Legal reasoning — practising attorneys, matched to contract, litigation, or regulatory work as the prompt requires.
- Mathematics and science — mathematicians and chemists who check each step, not just the conclusion.
- Code and systems — working engineers who run the code, read the design, and know what a reviewer would reject.
- Finance and accounting — analysts and accountants who know which plausible number is the wrong one.
Every rater is credential-checked when they join the eeko expert network, qualifies on your rubric before production, and is monitored against gold items throughout. Labels are tagged by source, so a model-suggested judgment is never passed off as a human one.
One preference batch, calibration to receipt.
An illustrative batch of clinical pairwise comparisons: four rubric dimensions, fifteen percent overlap.
From rubric draft to a signed batch.
Fixed scope, fixed price, pilot before volume. Standing orders run the same loop on a schedule as your policy changes.
Expert RLHF raters by industry and discipline.
Which raters, which rubric dimensions, and which handling rules all follow from the domain of the prompts.
RLHF preference data questions.
How do you measure inter-annotator agreement on preference data?
A share of items set in the specification goes to two or more raters. We compute agreement on that overlap for each batch and for each rubric dimension, using a chance-corrected statistic suited to the label type, and we put the figures in the delivery report rather than a footnote. Items under the agreed threshold are not averaged; they go to a senior reviewer, whose decision is recorded with the item. Over time the numbers also show which rubric lines need rewriting.
How are RLHF raters qualified?
Every rater in the eeko expert network is credential-checked in their field before they see any work. For each project they then sit a qualification set built from your rubric with known answers, and only those who clear the bar touch production items. Gold items are seeded into live work so drift shows up quickly, and a rater who slips below the bar is taken off the project. Each label stores which rater produced it, so you can re-weight or exclude a rater after delivery.
Do we send the prompts and completions, or do you?
Either. Most teams send prompts and completions sampled from their current policy, so the preferences reflect the model as it is today. If you want harder or rarer prompts than your logs contain, domain experts can write them. Anything you send is handled under the confidentiality terms of your agreement, kept out of the eeko catalogue, and deleted or returned on the schedule you set.
How much does RLHF preference data cost?
Per item, quoted after scoping. The drivers are rater expertise, item length and difficulty, how many rubric dimensions there are, whether you want rationales, and the overlap share for agreement. A physician comparing two long clinical answers costs more than a general-domain pairwise label. Larger and standing orders get volume pricing, and a pilot batch comes first so you can check the labels before committing.
What format is preference data delivered in?
JSON Lines by default, one record per comparison: the prompt, the completions, the preference or scores, the rationale, the rater ID, the rubric version, and the review status. Parquet for large volumes. If your trainer wants a particular schema, we write it into the spec and validate each delivery against it. Every bundle ships with a signed receipt naming each record by ID and hash.
Can you collect preferences on multi-turn conversations and agent trajectories?
Yes. Raters can judge whole conversations or tool-using agent runs rather than single turns, scoring each step, the final outcome, or both, as the rubric specifies. These items take longer and usually call for raters who have used the tools in question, which shapes both selection and price. It is the same kind of data we rely on when tuning the agents eeko deploys for its own clients.
What is the difference between RLHF and DPO data?
Not much, from the rater's side. Both start from a human preference between two or more completions of the same prompt. Classic RLHF trains a reward model on those preferences and then optimizes the policy against it; DPO skips the separate reward model and optimizes the policy on the pairs directly. The collection work, rater qualification, rationale capture, and agreement measurement are identical, and we deliver in a schema either pipeline can load.
Related data services.
Send us the pairs your raters cannot judge.
Five minutes to send a dataset request. Within one business day a data lead comes back with questions, a rater plan by domain, and a per-item price. No deck. A ranked list of what to label first.
Experienced within
Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: