Expert-vetted pairwise response rankings that teach your LLM to be helpful, honest, and safe — not just fluent. Every comparison pair includes structured annotator reasoning that goes beyond a simple binary choice.
RLHF (Reinforcement Learning from Human Feedback) is how today's most capable AI assistants — ChatGPT, Claude, Gemini — learned to be genuinely helpful rather than just statistically plausible. The core of RLHF is preference data: pairs of AI responses where a human expert judges which one is better and why.
When your language model generates two different answers to the same question, a human annotator compares them across multiple dimensions: helpfulness, factual accuracy, safety, tone, and cultural appropriateness. This judgment — and the structured reasoning behind it — is fed into a reward model that learns to predict what good responses look like. That reward signal then shapes your LLM's policy during reinforcement learning.
The quality of your preference data is the single biggest lever you have over your model's alignment. Low-quality data — where annotators pick responses that sound confident rather than ones that are correct, or reward agreeable-but-wrong answers (sycophancy) — actively makes your model worse. High-quality preference data, with clear guidelines, calibrated annotators, and verifiable kappa scores, is what separates aligned models from costly failures.
Concave AI specialises exclusively in this. We do not operate a general crowdsourcing platform. Every annotator on an RLHF project is a domain expert: a doctor comparing medical advice quality, a lawyer evaluating legal analysis accuracy, a software engineer assessing code correctness. Your preference pairs are judged by people who actually understand whether the answer is right — not just whether it reads well.
Our RLHF annotators are domain specialists — not crowdworkers. Every preference pair includes structured reasoning so your reward model learns from true signal.
Get a Free Audit →Expert annotators compare two model responses side-by-side, flagging hallucinations, sycophancy, and selecting the better aligned output.
Every RLHF project runs through the same rigorous process. No shortcuts on calibration, no lowering the kappa bar, no delivery without a QA report.
We do not deliver a CSV and disappear. Every RLHF project ships with a complete documentation package so your ML team can trust and verify what they are training on.
RLHF preference data is the correct investment when you need your model to make nuanced judgments — not just pattern-match on training examples.
No opaque enterprise quotes. Pricing is per preference pair based on domain and annotation depth. All projects start with a free 50-pair audit.
Request a Custom Quote →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.