Summary of Efficient Diffusion Transformer Policies with Mixture Of Expert Denoisers For Multitask Learning, by Moritz Reuss et al.
Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning
by Moritz Reuss, Jyothish Pari, Pulkit Agrawal, Rudolf Lioutikov
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Mixture-of-Denoising Experts (MoDE) policy for Imitation Learning offers a novel approach to addressing the computational roadblock posed by scaling laws in current Transformer-based Diffusion Policies. By combining efficient scaling with noise-conditioned self-attention mechanisms, MoDE achieves state-of-the-art performance on 134 tasks across four established imitation learning benchmarks (CALVIN and LIBERO). Compared to default Diffusion Transformer architectures, MoDE uses 90% fewer FLOPs and active parameters while surpassing CNN-based and Transformer Diffusion Policies by an average of 57%. The paper provides comprehensive ablations on MoDE’s components, offering insights for designing efficient and scalable Transformer architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoDE is a new way to teach machines to do things we can do. Right now, some models are getting very big to learn lots of things, but they take up too much computer power. To fix this, scientists created MoDE, which lets them use less computer power and still get good results. They tested it on lots of different tasks and found it works better than other ways of doing the same thing. By using MoDE, computers can learn new skills faster and with less energy. |
Keywords
» Artificial intelligence » Cnn » Diffusion » Scaling laws » Self attention » Transformer
