One training example via RLVR boosts LLM math reasoning from 17.6% to 35.7% average across six benchmarks.
Towards understanding grokking: An effective theory of representation learning.Advances in Neural Information Processing Systems, 35:34651–34663
2 Pith papers cite this work. Polarity classification is still indexing.
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Experiments on modular arithmetic with heavy label noise show that over-parameterized networks form a distributed internal generalization structure that can be extracted via frequency methods to achieve high accuracy despite 80% noise.
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Unveiling Memorization-Generalization Coexistence: A Case Study on Arithmetic Tasks with Label Noise
Experiments on modular arithmetic with heavy label noise show that over-parameterized networks form a distributed internal generalization structure that can be extracted via frequency methods to achieve high accuracy despite 80% noise.