Residual connections align cross-layer gradients while symmetry-breaking activations prevent rotational drift, causing principal singular vectors of adjacent layers to align.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Certain errors in proxy rewards for policy gradient methods can be benign or beneficial by preventing policies from stalling on outputs with mediocre ground truth rewards, enabling improved RLHF metrics and reward design insights.
citing papers explorer
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Why Geometric Continuity Emerges in Deep Neural Networks: Residual Connections and Rotational Symmetry Breaking
Residual connections align cross-layer gradients while symmetry-breaking activations prevent rotational drift, causing principal singular vectors of adjacent layers to align.
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When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient
Certain errors in proxy rewards for policy gradient methods can be benign or beneficial by preventing policies from stalling on outputs with mediocre ground truth rewards, enabling improved RLHF metrics and reward design insights.