Iterated Amplification

Iterated Amplification is a method for training AI systems by breaking down complex tasks into smaller, manageable parts. A human expert repeatedly assists the AI in solving these parts, gradually guiding it to understand and perform the full task. Over multiple rounds, the AI learns to combine these smaller skills into a competent overall ability. This approach leverages human judgment and feedback to amplify the learning process, making it possible for AI to handle intricate problems with greater accuracy, while reducing the need for the human to directly solve every aspect each time.