Reinforcement Learning

Also known as: RL

Reinforcement learning trains an agent to maximize expected reward through interaction with an environment. Policies map states to actions; learning uses returns, advantages, and exploration.

In LLMs, RL appears in RLHF/RLAIF-style alignment where the “environment” is a preference or reward model over generated text.

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