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cs.LG2026
Benign Overfitting for General Norms and Distributions
Daniel Barzilai, Ohad Shamir
Understanding why predictors can generalize despite interpolating noisy training data is a central puzzle in machine learning. Most work on such "benign overfitting" studies minimu…
cs.LG2026
When Is Compositional Reasoning Learnable from Verifiable Rewards?
Daniel Barzilai, Yotam Wolf, Ronen Basri
The emergence of compositional reasoning in large language models through reinforcement learning with verifiable rewards (RLVR) has been a key driver of recent empirical successes.…
cs.LG2026
Limitations of SGD for Multi-Index Models Beyond Statistical Queries
Daniel Barzilai, Ohad Shamir
Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. To that end, a commonly used tool…