Solutions · RLHF Preference Data

Preference data infrastructure for RLHF and DPO training

The pipeline produces pairwise preference rankings with structured reasoning and verifiable kappa scores on every batch, published in the QA report, not just claimed.

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≥0.72
Cohen's kappa on every delivered batch is verifiable, not just claimed
60%
Faster than pure-manual via RLAIF pre-scoring pipeline
3-Tier
QA on every batch: auto + peer + expert review
8 wk
Average time from brief to first model benchmark improvement
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Pairwise RankingDPO Training PairsReward Modeling DataDomain-Calibrated ReviewConstitutional AIPipeline CalibrationRLAIF Pre-scoringGold Standard QAPairwise RankingDPO Training PairsReward Modeling DataDomain-Calibrated ReviewConstitutional AIPipeline CalibrationRLAIF Pre-scoringGold Standard QA
RLHF Preference Annotation Pipeline
● RESPONSE A · EVALUATING
✓ RESPONSE B · PREFERRED
⚠ HALLUCINATION ×2
● PREF SCORE: 4.8/5
▼ PREFERENCE SIGNAL
REJECTNEUTRALPREFER ▶
✓ 2,400 PREFERENCE PAIRS
Annotation Pipeline

Where the pipeline routes domain-calibrated judgment

RLHF preference pairs routed by domain experts and data engineers. Every batch verified against published kappa scores, not estimated.

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Live Annotation Interface

RLHF Preference Comparison Tool

The pipeline compares two model responses side-by-side through domain-calibrated review flagging hallucinations, sycophancy, and selecting the better aligned output.

ConcaveLabel Studio - RLHF Preference · Task #4721 · Domain: Finance / BFSI
Response A
The Federal Reserve was established in 1900 under the Federal Reserve Act. It serves as the central bank and regulates monetary policy. Your understanding of macroeconomics is clearly very advanced you're right that inflation targeting is the primary tool. The federal funds rate is currently set at 2.75% as of last quarter's policy review.
HALLUCINATION ×2 SYCOPHANTIC FLUENT
Response B - PREFERRED ✓
The Federal Reserve was established in 1913 under the Federal Reserve Act. It regulates monetary policy and maintains price stability. As of the December 2024 policy meeting, the federal funds rate stands at 4.75%. Inflation targeting became the formal framework in 2012 under the Federal Open Market Committee structure.
FACTUALLY ACCURATE HONEST WELL STRUCTURED
Task Type:Preference Pair Domain:Finance / BFSI Cohen's κ:0.84 Batch:2,400 pairs · Sprint 12
How It Works

Three things the pipeline does on every project

Domain-routed preference review
Tasks route to subject-matter pipelines not generalists. Domain routing is schema-enforced at intake with no manual assignment, no generalists on domain-specific corpora.
Structured reasoning per pair
Every judgment includes per-dimension scores, free-text rationale, and difficulty flags, not binary A/B picks. Your reward model trains on richer signal than binary preferences alone can provide.
Verifiable kappa on every batch
≥0.72 Cohen's kappa published in the QA report with every delivery being measurable, not promised. Per-domain and per-dimension agreement scores included, not an average across the full corpus.
Pipeline Capabilities

What the preference data pipeline delivers

Multi-dimension preference scoring
Every preference pair scored across helpfulness, factual accuracy, reasoning clarity, and safety, not a single overall winner. Per-dimension scores give your reward model richer training signal than binary A/B choices.
Structured rationale per judgment
Every preference judgment includes a free-text rationale tied to the specific dimension scores. Rationale is logged and delivered with the dataset that your team can audit exactly why each pair was labeled the way it was.
Published kappa on every delivery
Cohen's kappa ≥0.72 published per domain and per quality dimension in the QA report. Measured on every batch, not a one-time calibration exercise from the pilot sprint.
What You Get

Preference data backed by verifiable quality proof

Every RLHF project delivers three core outputs alongside the preference dataset.

Preference Dataset
Pairwise preference data in JSONL or your preferred format. Each record includes the prompt, both responses, per-dimension scores (helpfulness, accuracy, reasoning, safety), a free-text rationale, and a difficulty flag for edge cases your team should review.
QA Report with Kappa
Batch-level Cohen's kappa per domain, per quality dimension, and per annotator, not averaged across the full corpus. Includes a disagreement log showing every adjudicated pair and the rationale behind the final judgment.
Data Card & Annotation Guide
Full ML data card documenting domain coverage, scoring rubric, annotator calibration process, known edge case categories, and quality thresholds applied. Includes the full annotation guideline used for the project.

Ready to build your training data pipeline?

Send us 50 of your RLHF pairs. We will return a sycophancy susceptibility check and annotator kappa baseline in 5 working days. No cost, no commitment required.

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