Mental BoundMost AI learns from labeled examples: "this is a cat," "this sentence means X." Reinforcement learning works differently. The AI tries things, gets feedback — a reward or a penalty — and gradually learns which actions lead to better outcomes. Think of teaching a dog: you don't explain the rules of fetch; you reward the behavior you want and ignore or correct the rest. Over many tries, the dog figures it out. RL does something similar, but with software.
The classic example is a game. An RL agent plays thousands of rounds, wins some and loses some, and over time discovers strategies that maximize its score. AlphaGo, which beat world champions at Go, learned largely through reinforcement learning — playing against itself and improving from the results. The same idea applies beyond games: robots learning to walk, trading algorithms learning to optimize returns, or chatbots learning which responses humans prefer.
RL is increasingly used to refine language models. A model might generate several possible replies; humans (or another model) rank them; the model gets a "reward" for producing responses that rank higher. This process — often called RLHF (reinforcement learning from human feedback) — helps align chatbots to be more helpful, harmless, and honest. The model isn't told the rules in advance; it learns them from the feedback.
The trade-off: RL can be slow and data-hungry, since the model needs many attempts to learn. It also risks reward hacking — finding shortcuts that maximize the score without actually solving the problem. Still, for tasks where you can define "good" and "bad" outcomes, RL is a powerful way to train systems that improve through practice.