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# Beyond VLAs: How World Action Models Reshape Robot Manipulation

**[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 7 min read · 0 upvotes · 0 comments

## Summary

World Action Models (WAMs) are proposed as a successor to Vision-Language-Action (VLA) models for robot manipulation. While VLAs use vision-language model backbones that describe the world, WAMs use video world models that predict how the world evolves, giving them a physics prior. NVIDIA's Cosmos 3 is presented as an open foundation model for building WAMs, featuring a Mixture-of-Transformers architecture trained on 767M images, 348M videos, and 8M action samples. Two post-trained policy models are available: Cosmos3-Nano-Policy-DROID (16B) for workstation serving and Cosmos3-Edge-Policy-DROID (4B) for on-device deployment on Jetson hardware. Benchmarks show the omni-pretrained checkpoint improved RoboLab success rates from 28.1% to 36.8% over a base checkpoint, demonstrating the value of the physical prior. WAMs require less task-specific data, generalize better to unseen environments, and adapt faster to new robot embodiments.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developer.nvidia.com/blog/beyond-vlas-how-world-action-models-reshape-robot-manipulation>

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Tags: [#robotics](https://daily.dev/tags/robotics)

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