RLHF & Preference Data

[ OVERVIEW ]

Structured preference data for frontier model training and alignment: side-by-side output comparisons, preference rankings with written rationale, instruction-response pairs, and chain-of-thought reasoning traces. Every rating is produced by a domain-verified specialist (medicine, law, code, science, finance, linguistics) with calibration against your gold standard before work begins. Agreement is tracked per rubric dimension. Built for RLHF, constitutional AI, reward model training, and evaluation benchmarking.

[ KEY HIGHLIGHTS ]

• Domain-expert raters verified in medicine, law, code, science, finance, linguistics
• Calibration against your gold standard before production rating begins
• Rating formats: side-by-side comparison, Likert scales, pairwise preference
• Written rationale captured per rating for reward model interpretability
• Chain-of-thought traces with step-level scoring for reasoning evaluation
• Inter-rater agreement tracked per rubric dimension and reported per delivery
• Instruction-response sets scoped by task type, domain, and difficulty tier

[ TECHNICAL SPECIFICATIONS ]

Data format

JSONL with prompt, response variants, rubric scores, rationale, rater ID, timestamp

Annotations

Preference rankings · Likert ratings · dimension-specific scoring · CoT step labels

Schema

OpenAI preference format · Anthropic HH format · Claude-RLHF-style · custom schemas

Licensing

Commercial RLHF and training rights · per-domain or cross-domain · IP framework per project