Just read a piece about Databricks working on this idea called TAO (Test-time Adaptive Optimization). Supposedly helps models improve themselves without clean labelled data — sort of like RL with synthetic practice rounds?

Sounds clever, but also feels like one of those things that might work great in theory and fall apart in the wild. Anyone come across it in more detail or seen use cases outside research circles?