Mini-diffuser reduces by an order of magnitude the time and memory needed to train multi-task vision-language robotic diffusion policies!

We introduce Mini-Diffuser (Hu et al., 2025), a method for training multi-task robot policies that can perform a variety of tasks using vision and language as input—while training significantly faster and using far less memory than previous approaches.

https://mini-diffuse-actor.github.io

The key insight comes from comparing how diffusion models are used in different domains. In image generation, diffusion models refine high-dimensional pixel data. In contrast, robot actions are much simpler, typically involving only 3D positions, rotations, and gripper states. However, the conditions—such as images and language instructions—remain high-dimensional. Mini-Diffuser takes advantage of this asymmetry. Instead of generating one action per input, it generates multiple action samples for the same vision-language input. This allows the model to train over 20× more efficiently with minimal extra cost. To support this strategy, we introduce lightweight architectural changes that prevent interference between samples during training. Mini-Diffuser offers a simple, fast, and effective recipe for training generalist robot policies at scale.