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Reinforcement Learning from Human Feedback(RLHF)
Fine-tuning a policy with reinforcement learning against a reward model learned from human preference judgments
In 30 seconds
- What
- Trains a reward model from human preference comparisons, then optimizes agent behavior against that model while constraining drift from baseline.
- When to use
- Aligning agent outputs to nuanced human values where simple metrics fail and you can afford substantial human annotation and compute.
- Watch out
- Reward model learns reviewer biases and edge cases poorly; agent exploits model gaps rather than genuinely improving, requiring expensive monitoring.
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Reinforcement Learning from Human Feedback: Overview
Fine-tuning a policy with reinforcement learning against a reward model learned from human preference judgments
- Human preference collection
- Reward model training
- Policy optimization with KL control
- Human-in-the-loop evaluation
- Behavior shaping from comparative judgments
- Reward-hacking monitoring
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References
The papers, specifications, and repositories this pattern is based on.
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