RLHF & preference data
RLHF and preference data from verified experts
Human preference data is only as good as the humans behind it. Pathwize routes pairwise comparisons to credential-verified experts and records the choice, the rationale, and who made it, as a signed trail you can audit.
Talk to us about rlhf & preference data
Where the usual approach falls short
You cannot see who judged your pairs
Most vendors hand back preference labels with no way to prove who produced them or how consistently. You are trusting the reward signal blind.
AI disguised as human feedback
When contributors quietly route pairs through an LLM, you train your model to imitate another model while believing you captured human judgment.
Generalists on expert prompts
Frontier preference work (medicine, law, security, maths) needs experts who can pick the better answer for the right reason, not crowd workers optimizing for speed.
What you get
Comparisons with a rationale
Every judgment carries the reason behind it, so you get the tie-breaker the expert applied, not just an A-or-B you cannot audit.
Credential-verified raters
Pairs are judged by license- and credential-verified specialists in the relevant domain, treated as experts rather than interchangeable gig workers.
Provenance by default
A hash-chained, signed trail records who judged each pair under what instructions, mapping cleanly to AI Act Annex IV.
Live inter-rater agreement
Multiple experts per pair where the judgment is subtle, with agreement measured in-flight so you catch drift before it reaches your reward model.
How the engagement runs
Specify the comparison
Define the prompts, the dimension to weigh (helpfulness, safety, accuracy), and the expertise required.
Matched expert panel
A credential-verified panel is assembled for your domain, vetted rather than crowd-sourced.
Judged with provenance
Experts compare pairs in a sandboxed workspace, each choice and rationale captured on a signed trail.
Auditable delivery
Receive preference data plus a reproducible provenance bundle ready for review.
What you receive
- Pairwise preference labels with per-item rationale
- Signed, hash-chained provenance for every judgment
- Inter-rater agreement metrics per batch
- Annex IV-ready documentation of methodology and human oversight
FAQ
RLHF & preference data, answered
Pairwise comparisons where an expert sees two model outputs for the same prompt and picks the better one, with a rationale. Enough of these train a reward model that captures human preference.
Work runs in a sandboxed environment with credential-verified experts and a signed provenance trail, so every judgment is attributable to a known, qualified person under known instructions.
Yes. Pairs are routed to license- and credential-verified experts in the relevant field, including medicine, law, security, and frontier mathematics.
Verifiable rlhf & preference data, on your data
Source expert data with provenance built in, EU-native and audit-ready. Book a demo with your ML and compliance teams.