Reinforcement Learning from Human Feedback (RLHF)
Definition
RLHF uses human preferences for subsequent reward-based optimization.
In the narrow sense, a training pipeline that converts human preference data into a learned reward signal and then optimizes a model policy with reinforcement learning. The term is sometimes used more broadly but is not synonymous with every direct preference optimization.
Simply explained
Humans compare answers, a rater learns from this and provides the signal for further learning steps.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
Humans compare answer pairs, a reward model learns preferences from this, and a policy is optimized against this signal.
Limits and pitfalls
- Equating RLHF with every preference adjustment or a fixed PPO pipeline, neglecting sampling and briefing of raters, or ignoring errors and reward hacking of the reward model.
Origin & history
RLHF combines preference learning and reinforcement learning; it was developed from the 2010s and widely used for language models from 2020 onward.
Use in mid-sized companies
- Possible application: Humans compare answer pairs, a reward model learns preferences from this, and a policy is optimized against this signal.
Frequently asked questions
What does Reinforcement Learning from Human Feedback (RLHF) mean?
In the narrow sense, a training pipeline that converts human preference data into a learned reward signal and then optimizes a model policy with reinforcement learning. The term is sometimes used more broadly but is not synonymous with every direct preference optimization. What mistakes should be avoided? Equating RLHF with every preference adjustment or a fixed PPO pipeline, neglecting sampling and briefing of raters, or ignoring errors and reward hacking of the reward model.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
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