Editing Openai/693c0f4f-255c-8008-92e9-0cd44c6d6226 (section)

==== In Sec. 5.2 they say: ====
* They take VAEs trained in § 5.1 (with a Transformer decoder) on PUG datasets. Those give: - A decoder f^\hat ff^, - An encoder g^\hat gg^ trained with a VAE objective on ID.

Then they:
# Apply generative replay (Eq. 4.4): - Use f^\hat ff^ to generate OOD images by recombining slots (like shuffling animal/background slots). - Train a new encoder on these synthetic images.
# Apply search (Eq. 4.3) at test time: - Use the encoder’s output as init, - Refine it by gradient-based optimization.

They show that:
* Non-generative approaches (encoder-only, even with lots of data) struggle with OOD combinations.
* Generative approaches using search and replay on top of a good decoder significantly boost OOD performance.