12 papers
Offline Materials Optimization with CliqueFlowmer
Jakub Grudzien Kuba, Benjamin Kurt Miller, Sergey Levine +1
Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials…
Cliqueformer: Model-Based Optimization with Structured Transformers
Jakub Grudzien Kuba, Pieter Abbeel, Sergey Levine
Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based…
What Really Matters in Matrix-Whitening Optimizers?
Kevin Frans, Pieter Abbeel, Sergey Levine
A range of recent optimizers have emerged that approximate the same "matrix-whitening" transformation in various ways. In this work, we systematically deconstruct such optimizers,…
A Stable Whitening Optimizer for Efficient Neural Network Training
Kevin Frans, Sergey Levine, Pieter Abbeel
In this work, we take an experimentally grounded look at neural network optimization. Building on the Shampoo family of algorithms, we identify and alleviate three key issues, resu…
Compute-Optimal Scaling for Value-Based Deep RL
Preston Fu, Oleh Rybkin, Zhiyuan Zhou +4
As models grow larger and training them becomes expensive, it becomes increasingly important to scale training recipes not just to larger models and more data, but to do so in a co…
Value-Based Deep RL Scales Predictably
Oleh Rybkin, Michal Nauman, Preston Fu +4
Scaling data and compute is critical to the success of modern ML. However, scaling demands predictability: we want methods to not only perform well with more compute or data, but a…