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Reinforcement Learning
AI Glossary Term
What is Reinforcement Learning?
Definition
Training by reward signals where an agent learns actions that maximize long-term return.
Related terms
Reinforcement Learning from Human Feedback (RLHF)
A training method that uses human preference signals to shape model behavior.
Reward Model
A model that scores outputs based on preference signals, often used in RLHF pipelines.
AI Agent
A software system that can observe, reason, and take actions to achieve a goal, often using tools and memory.
Constitutional AI
A training and behavior-shaping approach where model outputs are guided by a fixed set of written principles.
Agentic Loop
An iterative cycle where an AI agent observes, plans, acts, and reflects until it completes a goal or hits a stop condition.
DPO (Direct Preference Optimization)
A training method that fine-tunes models directly on preference pairs without needing a separate reward model.
Learn more in our free guides
Deep Learning
Reinforcement Learning
Machine Learning Basics
Supervised Learning
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