Author

Ge Zhang

2 papers indexed here

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Preprint Aug 2026

Tunneling the Loss Landscape: Bypassing Memorization with Monte Carlo Parameter Swapping

Grokking is a striking phenomenon in neural network training, where a model can undergo a prolonged period of pure memorization before abrupt generalization. While previous works have attempted to interpret it through classical machine learning mechanisms like weight norm, recent research draws an analogy from statisti...

Lai Shun Chan, Xiaotian Zhang, Yue Shang et al. · 1 citation

Jul 2026

The Grokked Illusion: True Equilibrium Mitigates Catastrophic Forgetting

While neural networks are typically evaluated by their training and test performance, these metrics do not reveal how robust a learned representation is. Recent studies have shown that solutions occupying larger volumes in parameter space, as quantified by Boltzmann entropy, often exhibit superior generalizability comp...

Xiaotian Zhang, Lai Shun Chan, Yue Shang et al. · arXiv.org · 0 citations