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Reinforcement Learning from AI Feedback(RLAIF)
Training a policy from preference judgments produced by an AI evaluator under human-defined criteria and oversight
In 30 seconds
- What
- Trains a policy using preference judgments from an AI evaluator operating under human-defined criteria, with human auditing to catch evaluator drift.
- When to use
- You need to scale preference labeling beyond human capacity while maintaining alignment with human values on a specific task.
- Watch out
- The AI evaluator can silently drift from the rubric or amplify human biases baked into the calibration set, corrupting the entire training run.
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Reinforcement Learning from AI Feedback: Overview
Training a policy from preference judgments produced by an AI evaluator under human-defined criteria and oversight
- AI-generated preference judgments
- Human-defined evaluation criteria
- Evaluator calibration against humans
- Reward model or direct feedback variants
- Policy optimization with drift controls
- Independent human auditing
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References
The papers, specifications, and repositories this pattern is based on.
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