Intelligence Engine Applications & Developer Ecosystems
RLAIF: Reinforcement Learning from AI Feedback
RLAIF uses a model as the judge to score outputs, replacing or augmenting human feedback in RLHF. It scales feedback collection and reduces cost, but inherits the judge's biases and can amplify them. RLAIF works best when the judge is well-calibrated, the reward is verifiable, and human review still spot-checks the results.
Pontos principais
- Use RLAIF where the judge model is strong on the target task.
- Calibrate the judge against human labels before trusting it.
- Prefer verifiable rewards (tests, schemas) over subjective preference.
- Spot-check AI feedback with human review on a sample.
- Watch for reward hacking: the policy gaming the judge's quirks.
- Keep a human-in-the-loop gate for safety-critical behaviors.
- Log judge scores and policy outputs for audit and debugging.
- Re-run calibration as models and tasks change.
Perguntas frequentes
RLAIF or RLHF?
RLHF for highest quality and safety; RLAIF for scale and cost where the judge is reliable.
Can the judge be the same model as the policy?
It can, but a separate or stronger judge reduces self-bias.
What is reward hacking?
The policy learns to score well without actually improving the desired behavior.
Is RLAIF production-ready?
For well-scoped tasks with verifiable rewards, yes; for open-ended alignment, keep humans involved.
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