The DiLoCo team at Google DeepMind and Google Research is proud to release Decoupled DiLoCo, the next frontier for resilient AI pre-training.
Decoupled DiLoCo enables training with datacenters across the world, using heterogeneous hardware, and never halting the system despite hardware failures.
New paper alert: "Latent Space Communication via K-V Cache Alignment" https://t.co/MyYYTvQs7z
We propose a method for Large Language Models to communicate directly via their internal states, bypassing the need for discrete text generation 🧵
Excited to announce our latest #ICLR work on long-context/relational reasoning evaluation for LLMs ReCogLab!
https://t.co/jRk6kJv4uK
https://t.co/TLOle1HJYr
Work with Andrew Liu, @priorupdates@gargi_balasu@neuro_kim and others at @GoogleDeepMind
@amydeng_ Pretty consistently I’ve found that the best “ML” engineers are the ones who push for the least ML — being knowledgeable enough to persuade people against unnecessary complexity makes the times you do use ML stand out in impact!
personal update: i’ve joined @GoogleDeepMind as a research engineer this week! excited to help improve large models’ ability to solve real-world problems
@_Dave__White_ Numerical Linear Algebra by Trefethen is my favorite. Great examples, intuitive explanations, and explicit connections called out between a new idea and those previously covered.
@EugeneVinitsky@araffin2@markus_with_k The graph in the README is episode return on cartpole:swingup! It's close/slightly worse than SAC. I ran it on a couple of other envs one-off but didn't want to add graphs for 1 random seed and didn't have compute access to run a bunch
@_Dave__White_ interesting.. I think for the system to resolve to something that looks like a typical distribution of skill you need the likelihood of non-transitivity to decay with rating. will think about this more