Concepts & capability filters
reinforcement learning from human feedback
RLHF aligns a model with human preferences in stages: train a reward model on ranked responses, then optimize the policy against that reward with reinforcement learning.
- Category
- Not classified
- Difficulty
- Not classified
- Aliases
- None tracked
- Last reviewed
- 2026-07-02
Key facts
- PPO is a common optimizer but not required — preference methods like DPO reach similar goals without an explicit RL loop.
- In practice it shapes refusal style, helpfulness, and instruction adherence.