An exciting new paradigm for generative modeling that acts as a third pretraining axis when added to existing generative models, and also enables end-to-end generation. Increasing exploration monotonically improves existing models across images, video, and language, and the gains grow with scale (7%→36% with data, 13%→23% with parameters). Concretely, Explorative Models (XMs) reach 6.2× sample efficiency, 4.1× FLOP efficiency, and 47% better parameter efficiency. Exploration also enables scaling generalization, and scaling how end-to-end existing models are. As end-to-end generative models, XMs match diffusion on control tasks with up to 256× less inference compute.
you can scale it based on the number of modes in your distribution, and keep it low when its not a big issue (e.g. 2/3 worked pretty well). also, this doesn't affect inference at all so on device can still be fast.
An exciting new paradigm for generative modeling that acts as a third pretraining axis when added to existing generative models, and also enables end-to-end generation. Increasing exploration monotonically improves existing models across images, video, and language, and the gains grow with scale (7%→36% with data, 13%→23% with parameters). Concretely, Explorative Models (XMs) reach 6.2× sample efficiency, 4.1× FLOP efficiency, and 47% better parameter efficiency. Exploration also enables scaling generalization, and scaling how end-to-end existing models are. As end-to-end generative models, XMs match diffusion on control tasks with up to 256× less inference compute.
I am the first author, feel free to ask questions!
…does K also have to scale? I’d care a lot about wall-clock and peak memory, especially for local/on-device inference.
you can scale it based on the number of modes in your distribution, and keep it low when its not a big issue (e.g. 2/3 worked pretty well). also, this doesn't affect inference at all so on device can still be fast.