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Synthetic (AI) preference data · Human preference data

Synthetic vs Human Preference Data (RLAIF vs RLHF Labels)

What is the difference between Synthetic (AI) preference data and Human preference data?

Synthetic preference data uses a language model to judge which response is better, while human preference data comes from people. Lee et al. (2023) reported RLAIF achieving results comparable to RLHF on summarization and dialogue tasks, and datasets like UltraFeedback scaled AI labels cheaply. Human labels remain the reference for auditing AI judges and for domains where judge models are unreliable.

By Sarath Kavuru · October 20, 2026

Comparison table

DimensionSynthetic (AI) preference dataHuman preference data
Cost and speedLow cost, fast to regenerateHigher cost, slower
Known biasesPosition, verbosity, self-preference of the judge (Zheng et al., 2023)Annotator fatigue, guideline ambiguity, expertise gaps
EvidenceRLAIF ≈ RLHF on tested tasks (Lee et al., 2023)InstructGPT and most early RLHF systems
Example datasets/methodsUltraFeedback; Constitutional AIInstructGPT comparisons
Role in practiceBulk labelingGold sets, audits, specialist domains

When to use each

Synthetic (AI) preference data: Use AI labels for scale where a judge model has been validated against human labels on your task.

Human preference data: Use human, and where needed expert, labels to build the validation set and for domains beyond the judge model’s competence.

Frequently asked questions

Is synthetic preference data as good as human data?
On the tasks Lee et al. (2023) tested, RLAIF matched RLHF. That result does not automatically transfer to specialist domains, so validate an AI judge against human labels before relying on it.
What is Constitutional AI?
An Anthropic method where a model critiques and revises its outputs against written principles, and AI-generated preference labels based on those principles are used for RL.

References

  1. Lee et al., 2023. RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (arXiv:2309.00267)
  2. Cui et al., 2023. UltraFeedback: Boosting Language Models with Scaled AI Feedback (arXiv:2310.01377)
  3. Bai et al., 2022. Constitutional AI: Harmlessness from AI Feedback (arXiv:2212.08073)
  4. Zheng et al., 2023. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena (arXiv:2306.05685)
  5. Wang et al., 2022. Self-Instruct: Aligning Language Models with Self-Generated Instructions (arXiv:2212.10560)
  6. Ouyang et al., 2022. Training language models to follow instructions with human feedback (arXiv:2203.02155)

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