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RLAIF on a Small Task

Replace the human grader with a stronger model and see how close — and how cheap — you can get.

Key Insight​

This project trains a small model with DPO using preference labels generated by another LLM rather than by humans — RLAIF (Reinforcement Learning from AI Feedback) — and compares both the resulting quality and the labeling cost against a human-labeled baseline.

Why This Matters​

Human preference data is the slowest and most expensive part of RLHF; if AI judgments can match human ones on a task, the alignment pipeline gets dramatically cheaper, which is why RLAIF and Constitutional AI underpin most modern alignment recipes.