Reference · Glossary
Reinforcement learning
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Training by **trial and reward** — an agent takes actions, gets feedback, and learns policies that maximize long-term score.
When to use
Game-playing bots, robotics control, ad bidding, and alignment techniques like RLHF that rank preferred answers.
When not to
Simple prediction from labeled data — that's usually supervised learning, not RL.
Example
After initial training, humans rank two model replies; RLHF nudges the model toward answers people preferred over time.