Circuit Synchronization Precedes Generalization: A Causal Precursor to Grokking
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Abstract (arXiv)
Grokking is the delayed generalisation phenomenon where a transformer trained on modular arithmetic abruptly transitions from near-chance to near-perfect validation accuracy. It has been attributed to a Fourier-based algorithmic circuit, but its timing, causal structure, and controllability remain poorly understood. We introduce the Frequency Synchronization Degree (FSD), a normalised, permutation-tested metric for Fourier circuit synchronisation requiring no prior knowledge of the circuit. Across nine modular addition configurations (five primes, three seeds), FSD reaches its post-grokking level 500 to 3000 steps before grokking (mean lead 1722 steps, every configuration positive, sign-test p approx 0.004), and synchronises before a restricted-logit loss baseline in all nine cases, making it the earliest available predictor. We give direct causal evidence that the inter-phase gap is a regularisation phenomenon: forking training at the FSD-ceiling step and varying weight decay lambda produces monotonically earlier grokking, with delta-t proportional to 1/lambda. This law replicates across three primes (R-squared 0.89 to 0.99 on seed-averaged delta-t); per-run R-squared is unstable due to the chaotic transition, so we report error bars rather than single runs. Grokking occurs at a near-constant memorisation norm across lambda, grounding the constant in a threshold mechanism. This is not an artefact of applying a Fourier detector to a Fourier circuit: on the non-abelian group S5, a basis-faithful generalisation of FSD precedes grokking on all six seeds, while the original Fourier FSD does not. Using the FSD ceiling to schedule a weight-decay increase also accelerates grokking over a fixed schedule without destabilising training. An attention-only variant groks with a strong FSD precursor while an MLP-only model never groks.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 22 references that are in the map. First posted on arXiv 11 Jun 2026.
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21 of 22 references.
References · 21
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9 more
- 2023A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operationscited by 141
- 2023Explaining grokking through circuit efficiencycited by 105
- 2023Grokking as the Transition from Lazy to Rich Training Dynamicscited by 102
- 2022The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenoncited by 90
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- 2023Unifying Grokking and Double Descentcited by 49
- 2024Grokfast: Accelerated Grokking by Amplifying Slow Gradientscited by 38
- 2023Grokking Group Multiplication with Cosetscited by 22
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Citing papers per year: 2026: 2.
2 papers.