1 citations · 1 across the 4 of their papers we have counts for
5 papers
RLVR landscapes for iterated multiplications can be benign: Insights from spin-glass theory
Noa Rubin, Zohar Ringel
Despite the importance of reinforcement learning with verifiable rewards (RLVR), the extent to which it can learn new reasoning capabilities remains debated. Here we study the opti…
Mitigating the Curse of Detail: Scaling Arguments for Feature Learning and Sample Complexity
Noa Rubin, Orit Davidovich, Zohar Ringel
Two pressing topics in the theory of deep learning are the interpretation of feature learning (FL) mechanisms and the determination of implicit bias of networks in the rich regime.…
Applications of Statistical Field Theory in Deep Learning
Zohar Ringel, Noa Rubin, Edo Mor +2
Deep learning algorithms have made incredible strides in the past decade, yet due to their complexity, the science of deep learning remains in its early stages. Being an experiment…
From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning
Noa Rubin, Kirsten Fischer, Javed Lindner +5
Feature learning in neural networks is crucial for their expressive power and inductive biases, motivating various theoretical approaches. Some approaches describe network behavior…
Grokking as a First Order Phase Transition in Two Layer Networks
Noa Rubin, Inbar Seroussi, Zohar Ringel
A key property of deep neural networks (DNNs) is their ability to learn new features during training. This intriguing aspect of deep learning stands out most clearly in recently re…