Learning from human preferences

An AI agent learned to do a backflip by using human comparisons of behavior clips to infer a reward model.

It started with random actions, then improved through reinforcement learning guided by those preference judgments.

The system selectively asked for feedback on uncertain cases, making learning more sample-efficient.

This approach shows how AI can learn complex tasks from small amounts of human feedback without hand-designed rewards.