An Exploratory Replica-Overlap Probe of the Grokking Transition
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Abstract (arXiv)
We trained 64 independently seeded networks in four configurations, continuing each to sustained convergence or a 40,000-epoch ceiling. We then asked whether an RSB-inspired distribution of pairwise weight overlaps changes across the grokking transition. It is the alignment step, not the overlap statistic, that determines what this registered probe can report. The registered implementation permutes hidden units without the corresponding bias and head-internal permutations and therefore does not preserve the network function. Every q_wt value computed through this alignment inherits the defect; q_fn does not, because it is computed from predictions of the unpermuted models. The numerical-precision requirement also failed, and an audit found protocol deviations. Consequently, the pre-registered rule gives no verdict: registered outcome UNDETERMINED (reason code C0_INSTRUMENT_INVALID). These data provide neither a confirmatory null nor a validated reading of the Parisi order parameter. Only frac40 cleared the 12/16 checkpoint-completeness requirement. For this configuration, a post-hoc criterion applied to the same data gave a Hartigan-dip interval containing zero (95% CI for Delta dip = [-0.017, 0.034]), whereas the overlap standard deviation increased by a factor of about 5.6. A post-hoc calibration assigns the dip test zero power at the simulated separations; the interval is therefore uninformative, not evidence of no change. The standard-deviation ratio is the only statistic here with power at the observed effect. Ensemble loss was near-flat only under the pre-specified 1% threshold. Finally, grokking rates of 0/16, 11/16 and 16/16 remain descriptive because train fraction is confounded with split identity.
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References · 20
- 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,250
- 2023Progress measures for grokking via mechanistic interpretabilitycited by 822
- 2019Linear Mode Connectivity and the Lottery Ticket Hypothesiscited by 627
- 2018Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNscited by 585
- 2018Essentially No Barriers in Neural Network Energy Landscapecited by 328
- 2021The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networkscited by 285
- 2023Grokking as a First Order Phase Transition in Two Layer Networkscited by 48
- 2022Grokking phase transitions in learning local rules with gradient descentcited by 28
- 2024Simultaneous linear connectivity of neural networks modulo permutationcited by 9
- 2025Is Grokking a Computational Glass Relaxation?cited by 8
- 2026Early-Warning Signals of Grokking via Loss-Landscape Geometrycited by 6
- 2024Exploring Loss Landscapes through the Lens of Spin Glass Theorycited by 4
8 more
- 2026A Basin-Selection Perspective on Grokking via Singular Learning Theorycited by 4
- 2018Replica Symmetry Breaking in Bipartite Spin Glasses and Neural Networkscited by 3
- 2024Fundamental operating regimes, hyper-parameter fine-tuning and glassiness: towards an interpretable replica-theory for trained restricted Boltzmann machinescited by 3
- 2026Tunneling the Loss Landscape: Bypassing Memorization with Monte Carlo Parameter Swappingcited by 1
- 2026Dataset Complexity Shapes Finite-Distance Loss Geometry in Neural Networkscited by 1
- 2026Mapping the Emergence of Regularization-Driven Dynamics in Grokkingcited by 1
- 2026A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decaycited by 1
- 2026Quantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokkingcited by 1
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