# RLHF

> Reinforcement Learning from Human Feedback — training a model to prefer responses humans rate higher, aligning it with human intent.

_The Agentic Web Lexicon · /glossary/rlhf · [JSON](/api/glossary/rlhf) · [all The Agentic Web Lexicon](/glossary)_

- **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-tuning, grounding, transformer
- **etymology_origin:** — (verify-against-primary-at-build)
- **related_to:** fine-tuning, grounding, transformer
- **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