Reinforcement learning from human feedback
In machine learning, reinforcement learning from human feedback, including reinforcement learning from human preferences, is a technique that trains a "reward model" directly from human feedback and uses the model as a reward function to optimize an agent's policy using reinforcement learning through an optimization algorithm like Proximal Policy Optimization.