RLHF & preference data.
Human preference ranking, RLHF, and DPO preference data for teams fine-tuning generative models — produced by trained annotators working against your guidelines, not an anonymous crowd.
Preference data for fine-tuning, at every stage.
Only capabilities Ayta can currently deliver or is genuinely prepared to pilot are listed below.
Preference ranking
Annotators compare pairs or sets of model responses and rank them against your defined criteria.
RLHF
Human preference data structured for reinforcement learning from human feedback pipelines.
DPO preference data
Chosen/rejected response pairs produced for direct preference optimization fine-tuning.
SFT
Demonstration data and reference responses produced for supervised fine-tuning.
Need to evaluate model outputs directly? See Model Evaluation.
From guidelines to preference data.
Every RLHF program follows the same operating sequence.
Guideline intake
Your preference criteria and comparison rubric are built into the task before ranking starts.
Rater calibration
Annotators are trained and tested against your guidelines until judgments are consistent.
Structured comparison
Responses are ranked or compared head-to-head against the defined criteria, not gut feel.
Inter-rater agreement
Agreement between raters is tracked, and disagreement is resolved against your standard.
Delivery
Preference data delivered in the format your fine-tuning pipeline expects — RLHF, DPO, or SFT.
Have a data requirement?
Give us the specification — we'll build the operation around it.
Start a Pilot →