Reinforcement Learning

A machine learning method that trains a model to optimize its actions within a given environment to achieve a specific goal, guided by feedback mechanisms of rewards and penalties. This training is often conducted through trial-and-error interactions or simulated experiences that do not require external data. For example, an algorithm can be trained to earn a high score in a video game by having its efforts evaluated and rated according to success toward the goal.

Reinforcement learning with human feedback combines reinforcement learning with human feedback during the training process. Human feedback is provided on the model's output, often by comparing different outputs for the same prompt and indicating which output aligns better with human preferences. In reinforcement learning, the model learns by receiving rewards or penalties. Combining this with human feedback provides an additional source of rewards and penalties and helps align the Al's behavior with human preferences and values.