---
格式版本: 2
标题: "Flex-$π$: A Multi-Stream World-Action Model with Compute Flexibility"
原文链接: "https://arxiv.org/abs/2608.10860"
发布日期: "2026-08-11"
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AI优质: "否"
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去重键: "https://arxiv.org/abs/2608.10860"
---

## Computer Science > Robotics

## Title:Flex-$π$: A Multi-Stream World-Action Model with Compute Flexibility

Authors:[Ge Yan](https://arxiv.org/search/cs?searchtype=author&query=Yan,+G), [Jinghao Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+J), [Yuzhi Fan](https://arxiv.org/search/cs?searchtype=author&query=Fan,+Y), [Lei Cai](https://arxiv.org/search/cs?searchtype=author&query=Cai,+L), [Minwen Liao](https://arxiv.org/search/cs?searchtype=author&query=Liao,+M), [Jesse Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+J), [Dieter Fox](https://arxiv.org/search/cs?searchtype=author&query=Fox,+D)

[View PDF](https://arxiv.org/pdf/2608.10860) [HTML (experimental)](https://arxiv.org/html/2608.10860v1)

> Abstract: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- $\pi$, 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 a Mixture-of-Transformers backbone; per-stream dropout with cross-modality forcing then lets a single trained checkpoint run on any subset of these streams, from a fast action-only mode to full joint generation. The result is a policy that is exceptionally demonstration-efficient and generalizes well, beating the strongest baselines by up to 2-7 $\times$ on dexterous, precise, real-world bimanual manipulation tasks both in and out of distribution, all while running faster than $\pi_{0.5}$. Our project website: [this https URL](https://flex-pi.github.io/)

| Comments: |  |
| --- | --- |
| Subjects: | Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | [arXiv:2608.10860](https://arxiv.org/abs/2608.10860) \[cs.RO\] |
|  | (or [arXiv:2608.10860v1](https://arxiv.org/abs/2608.10860v1) \[cs.RO\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10860](https://doi.org/10.48550/arXiv.2608.10860) |

## Submission history

From: Ge Yan \[[view email](https://arxiv.org/show-email/73a31d64/2608.10860)\]  
**\[v1\]** Tue, 11 Aug 2026 12:33:51 UTC (17,493 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10860) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
