Flex-$π$: A Multi-Stream World-Action Model with Compute Flexibility
World-action models (WAMs) predict the future to act better, but nearly all of them predict only RGB latents, trained purely for pixel reconstruction, with no explicit signal for the 3D geometry or object semantics manipulation needs. We find a surprising free lunch: the same frozen video-generation VAE that encodes RGB also encodes 3D pointmaps almost losslessly, with no pointmap-specific training at all. This lets us supervise Flex-$π$, a 6B-parameter WAM, on 3D geometry and object-centric DINO semantics alongside RGB, at no cost in new sensors, new pre-training, or inference latency. Every visual signal is projected into this shared latent space and denoised jointly with actions inside...
| Authors | Ge Yan, Jinghao Liu, Yuzhi Fan et al. |
|---|---|
| Primary category | cs.RO |
| Published | 11 August 2026 |
| Last updated at source | 29 August 2026 |
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