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Synthetic preference data

As explained in Reinforcement Learning from Human Feedback

Synthetic preference data is preference data generated by models or algorithmic processes rather than directly collected from human judgment.

Synthetic preference data records which of two or more model completions should be preferred, with labels produced by a model or an automatic procedure rather than direct human judgment. It is widely used for preference fine-tuning and RLHF because pairwise comparisons provide a practical proxy reward when human values cannot be written as a precise reward function. section 8.4 section 11.5

A typical pipeline samples candidate answers for a prompt, selects a chosen and rejected completion with a frontier-model judge or a verifiable rule, and trains on the resulting preference pairs. AI feedback can approximate human preference labels at much lower cost, while strong models can generate and judge data at scale. section 12.7 section 12.1

Construction choices determine what behavior the training signal reinforces:

  • Keep some completions on-policy: generate them from the model being fine-tuned, possibly alongside a broader model pool, so contrastive training covers its actual token space. section 8.4 section 11.5
  • Control the chosen–rejected gap. A delta-learning view emphasizes the difference between the responses over the precise origin model; one demonstrated pairing uses a larger model’s answer as chosen and a smaller model’s answer as rejected. section 8.4
  • Exploit structured tasks when correctness or compliance is automatically checkable, including mathematical answers and instruction-following constraints such as word count, punctuation, or formatting. section 11.2.4
  • Audit systematic judge preferences before scaling. Length bias and preference for stylistically similar outputs can make chosen responses disproportionately resemble strong judge-adjacent models. section 8.4 section 11.5

Constraint-generated pair

For “Write a short poem about a goldfish. Start each sentence with g,” generate one answer with the constraint and another without it. Use the compliant poem as chosen and the noncompliant response as rejected, while retaining the full constrained prompt in the dataset. The pair teaches adherence to the constraint rather than only a general preference for poetic quality. section 11.2.4

Synthetic preference data is often the practical starting point for direct-alignment algorithms: implementation is simpler and performance can be strong relative to RL-based preference fine-tuning. It is not a substitute for every human-data need. Synthetic labels are comparatively low-noise but high-bias, so repeated judge tendencies can create unintended second-order effects; scaled pipelines may eventually combine AI and human feedback. section 8.4 section 12.4.1

Common datasets include UltraFeedback, Tülu 3, SmolLM 3, and Dolci Pref. Dataset scale ranges from smaller quickstart corpora to million-prompt collections with much longer responses, making generation, filtering, and bias monitoring operational concerns rather than merely labeling details. section 8.4 section 12.1

How Reinforcement Learning from Human Feedback covers it

How deep it goes
Details pair construction, on-policy sampling, delta learning, structured constraints, and judge-bias trade-offs without specifying a training loss.
Examples
Uses UltraFeedback, Tülu 3, SmolLM 3, Dolci Pref, and a constrained goldfish-poem pair.
What it assumes
Assumes familiarity with model completions and preference fine-tuning while motivating pairwise comparisons as reward proxies.
What you can do after
Apply it via direct-alignment algorithms by constructing and auditing chosen–rejected completion pairs.
How it connects
Connects direct-alignment algorithms, RLHF, RLAIF, on-policy learning, instruction following, and LLM-as-a-judge evaluation.
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