# Late-Stage Generalization Collapse in Grokking: Detecting anti-grokking with Weightwatcher

arXiv: https://arxiv.org/abs/2602.02859 · HTML: https://mlsanity.com/paper/2602.02859/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Progress measures for grokking via mechanistic interpretability](https://mlsanity.com/paper/2301.05217/) (2023)
- [Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets](https://mlsanity.com/paper/2201.02177/) (2022)
- [Towards Understanding Grokking: An Effective Theory of Representation Learning](https://mlsanity.com/paper/2205.10343/) (2022)
- [Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning](https://mlsanity.com/paper/1810.01075/) (2018)
- [The Heavy-Tail Phenomenon in SGD](https://mlsanity.com/paper/2006.04740/) (2020)
- [The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers](https://mlsanity.com/paper/2210.06313/) (2022)
- [Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data](https://mlsanity.com/paper/2002.06716/) (2020)
- [Explaining grokking through circuit efficiency](https://mlsanity.com/paper/2309.02390/) (2023)
- [A Tale of Two Circuits: Grokking as Competition of Sparse and Dense Subnetworks](https://mlsanity.com/paper/2303.11873/) (2023)
- [SETOL: A Semi-Empirical Theory of (Deep) Learning](https://mlsanity.com/paper/2507.17912/) (2025)
- [Grokking and Generalization Collapse: Insights from textttHTSR theory](https://mlsanity.com/paper/2506.04434/) (2025)
- [A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness](https://mlsanity.com/paper/2309.03004/) (2023)
- [The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks](https://mlsanity.com/paper/2104.06153/) (2021)

## Built on by (most cited first, 2 of 2)

- [Weight Decay Regimes in Grokking Transformers: Cheap Online Diagnostics](https://mlsanity.com/paper/2605.20441/) (2026)
- [At-Grok Is Not Converged:A Measurement-Validity Audit for Grokking Representation Metrics](https://mlsanity.com/paper/2607.06639/) (2026)

## Abstract (arXiv)

Memorization in neural networks lacks a precise operational definition and is often inferred from the grokking regime, where training accuracy saturates while test accuracy remains very low. We identify a previously unreported third phase of grokking in this training regime: anti-grokking, a late-stage collapse of generalization. We revisit two canonical grokking setups: a 3-layer MLP trained on a subset of MNIST and a transformer trained on modular addition, but extended training far beyond standard. In both cases, after models transition from pre-grokking to successful generalization, test accuracy collapses back to chance while training accuracy remains perfect, indicating a distinct post-generalization failure mode. To diagnose anti-grokking, we use the open-source textttWeightWatcher tool based on HTSR/SETOL theory. The primary signal is the emergence of Correlation Traps: anomalously large eigenvalues beyond the Marchenko--Pastur bulk in the empirical spectral density of shuffled weight matrices, which are predicted to impair generalization. As a secondary signal, anti-grokking corresponds to the average HTSR layer quality metric α deviating from 2.0. Neither metric requires access to the test or training data. We compare these signals to alternative grokking diagnostic, including ℓ2 norms, Activation Sparsity, Absolute Weight Entropy, and Local Circuit Complexity. These track pre-grokking and grokking but fail to identify anti-grokking. Finally, we show that Correlation Traps can induce catastrophic forgetting and/or prototype memorization, and observe similar pathologies in large-scale LLMs, like OSS GPT 20/120B.