Self-Play

A training technique in reinforcement learning where a model improves by competing against copies of itself rather than against human opponents or a fixed environment. Self-play generates an open-ended stream of training experience that scales with the model's own capabilities — the approach that enabled AlphaGo and AlphaZero to reach superhuman performance in games with no human data beyond the rules.