RLHF & Human Feedback Services
High-agreement human preference data for aligning large language models. Digitive runs managed human-feedback programs — preference and pairwise ranking, response comparison and reward-model data — delivered by calibrated, domain-expert evaluators rather than an anonymous crowd.
Why human feedback decides model quality
A model is only as aligned as the human preferences it learns from. When preference data comes from an anonymous crowd, agreement between evaluators is often low — and low agreement leaves real capability gains on the table.
Specialist evaluators who understand the domain surface the nuance that general crowdworkers miss, and produce preference data consistent enough to actually improve a model.
A managed team, not a crowd
Digitive delivers RLHF work through a managed, vetted team model. Evaluators are qualified by domain, calibrated on your rubric before live work, and held to a quality standard that is measured, not assumed.
Quality control stays in-house across the program — so preference data is reviewed, agreement is tracked, and issues are corrected before the data reaches your team.
What we deliver for RLHF programs
The building blocks of a human-feedback pipeline, delivered as a managed program.
Preference & pairwise ranking
Side-by-side comparison of model responses, ranked by expert evaluators against your criteria.
Response comparison + reason capture
Structured comparisons with written rationale, so a preference is explainable — not just a label.
Reward-model data
Preference and ranking signals prepared as data to train and refine reward models.
SFT demonstration data
Instruction–response demonstrations supplied as supporting context alongside preference work.
Calibrated expert evaluators
Vetted, domain-qualified evaluators calibrated before they touch live tasks.
RLHF-specific quality assurance
Multi-tier review, agreement tracking and re-grading built around preference tasks.
How we run a human-feedback program
A structured workflow from rubric setup to delivery — with calibration and quality control built in at every stage.
Task & rubric setup
We work from your use cases to structure tasks and the rubric evaluators apply.
Evaluator qualification
Specialists are vetted and tiered by domain and skill before assignment.
Calibration
Calibration rounds align evaluators on the rubric before and during live work.
Response comparison & preference ranking
Evaluators compare responses and produce ranked preferences with rationale where required.
Quality review
Senior QC leads review output with per-annotator quality dashboards.
Agreement tracking & re-grading
Inter-annotator agreement is tracked; lower-performing work is re-graded and escalated.
Delivery & iteration
Preference and reward-model data is delivered on a cadence, with calibration cycles feeding the next batch.
Preference data and response comparison
At the core of RLHF is a simple, repeated act done well: an evaluator sees a prompt and two or more model responses, and decides which is better — and why.
Done at scale with consistency, those comparisons become the preference and reward-model data your post-training pipeline needs.
Pairwise & multi-response comparison
Responses are compared side by side against defined criteria.
Ranking with rationale
Evaluators rank responses and, where required, capture the reason for the preference.
Reward-model data
Ranked preferences are prepared as signals to train reward models.
Consistency across evaluators
Rubrics and calibration keep judgments comparable across the team.
Selecting and calibrating evaluators
Preference data is only as good as the people producing it. Evaluators are vetted and tiered by domain and skill, then calibrated against your rubric before they work on live tasks — with calibration rounds continuing throughout the engagement.
Vetted specialists
Domain-qualified evaluators, tiered by skill and subject expertise.
Calibrated before live work
Rubric alignment and knowledge checks before any task is scored.
Ongoing recalibration
Regular calibration syncs keep the team aligned as work continues.
Quality assurance built for preference tasks
Preference work has its own failure modes. Our quality methodology is designed around them.
Calibration rounds
Evaluators calibrate against the rubric before live work and re-sync on a regular cadence.
Blind gold-set checks
Known-answer items are injected into live work to monitor evaluator reliability.
Independent QC
A dedicated quality layer reviews output — quality control is not subcontracted away.
Inter-annotator agreement
Agreement between evaluators is measured and monitored across the program.
Evaluator retraining
Lower-quartile work is re-graded and evaluators are re-briefed on recurring issues.
Multi-tier final review
Preference data passes multiple review layers before it reaches your team.
Domain-specialist feedback where it matters
Some responses can only be judged by someone who knows the field. For specialist content — such as code, math and reasoning — preferences are collected from evaluators with the relevant expertise, not generalists guessing at correctness.
That domain lens is what makes preference data trustworthy for models being pushed toward expert-level performance.
Code & engineering
Math & reasoning
Technical domains
And more
Scaling a program without losing quality
Ramping a human-feedback program usually means a trade-off between speed and quality. We ramp from a pre-vetted specialist bench, hold calibration cycles as the team grows, and keep per-evaluator quality visible throughout — so the program scales while agreement holds.
Delivered RLHF engagement — frontier AI lab
Proven at frontier-model scale
150+
PhD & Masters evaluators on the program
85%+
Inter-annotator agreement on preference ranking
60 days
From kickoff to full production scale
Figures describe a specific delivered engagement for a frontier AI lab.
How RLHF fits with evaluation and fine-tuning
RLHF is one layer of post-training. It sits alongside supervised fine-tuning (SFT) — which teaches a model from demonstrations — and alongside broader AI & LLM evaluation services that judge how well a model actually performs.
Many teams pair human feedback with adversarial safety testing. Where that applies, our AI red teaming work runs under the same managed team model, so preference data and safety findings come from one coordinated program.
RLHF in practice
How Digitive scaled a preference-ranking program for a frontier LLM lab.
RLHF questions, answered
Discuss an RLHF program
Tell us about your model and your alignment goals. We’ll scope a human-feedback program built around your rubric and your domains.