Reinforcement learning

In reinforcement learning, an agent interacts with an environment – such as a game or simulation – and receives rewards or penalties. Over many rounds it learns a strategy that maximises the reward.

The method became well known through AI systems that master board games such as Go. Today it also plays an important role in post-training language models, for example in RLHF and in models that “think” step by step.