How do unsupervised learning systems leverage self-play concepts?

They generate learning signals by having models predict or compete against their own outputs.

Self-play in unsupervised learning involves models generating their own training data or tasks. This includes predicting future states, reconstructing corrupted inputs, or competing against past versions of themselves. It is widely used in reinforcement learning and self-supervised representation learning to create infinite training signals without external labels.