RLHF
Reinforcement Learning from Human Feedback — training a model to prefer responses humans rate higher, aligning it with human intent.
RLHF shapes how an agent behaves once deployed on the agentic web — how it follows instructions, respects constraints and handles ambiguity.
- term
- RLHF
- category
- knowledge-memory
- short_def
- Reinforcement Learning from Human Feedback — training a model to prefer responses humans rate higher, aligning it with human intent.
- long_def
- RLHF fine-tunes a model using human preference data: people rank model outputs, a reward model learns those preferences, and reinforcement learning nudges the model toward higher-rated behavior. It is a core step in turning a raw pretrained model into a helpful, instruction-following assistant.
- see_also
fine-tuninggroundingtransformer- etymology_origin
- — verify-against-primary-at-build ↗ https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback — popularized by InstructGPT (OpenAI, 2022)
- related_to
fine-tuninggroundingtransformer- contrast_with
- Unlike supervised fine-tuning on fixed target answers, RLHF optimizes against a learned reward model of human preference, shaping style and safety rather than copying exact outputs.
- example
- RLHF is a large part of why a modern assistant follows instructions and declines unsafe requests instead of merely completing text.
- source
- https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback
- status
- active
- why_it_matters
- RLHF shapes how an agent behaves once deployed on the agentic web — how it follows instructions, respects constraints and handles ambiguity.
- sameAs
https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback- bridge_entity
- fine-tuning
- last_verified
- 2026-07-06
- md_twin
- /glossary/rlhf.md
last verified · by Özden Erdinc