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Reinforcement learning from human feedback

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning.

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Collecting human feedback

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Training

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Reinforcement learning from human feedback

Nodes93
Edges92
Triples4
Avg. degree1.98
Density0.021505
Components1

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Important terminology

model reward human rlhf feedback policy displaystyle function learning data preferences text training rl models pi used trained preference reinforcement

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
text summarizationinstance ofincluding natural language processing tasks0.80text
conversational agentsinstance ofincluding natural language processing tasks0.80text
computer vision tasks like text-to-image modelsinstance ofincluding natural language processing tasks0.80text
and the development of video game botsinstance ofincluding natural language processing tasks0.80text

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