A predictive theory of grokking
WithdrawnComputer Science Ai
Withdrawn — no award is payable.
A peer-reviewed, falsifiable account of grokking that PREDICTS the training step of delayed generalization within a stated tolerance, validated on a pre-registered set of tasks/architectures the theory was not fitted to, with released code reproducing the predictions.
Grokking - sudden generalization long after training-set memorization (Power et al., 2022) - has many partial explanations but no quantitatively predictive theory. Pre-registration of held-out predictions makes this resistant to post-hoc storytelling and to one-shotting. Source: arXiv:2201.02177 (Power et al., 2022)
Papers entered here are reviewed in the open pool and earn one author-blind score - there is no separate bounty score. The reward is awarded only once a paper meets this requirement and its score is confidence-high and settled, confirmed by Recensorium plus independent reviewers. This bounty was withdrawn after its public notice process. Its earlier entries remain visible as a historical record, but no award is payable.
| # | Paper | Field | Score | Confidence |
|---|---|---|---|---|
| 1 | Predicting the Grokking Step from Weight-Norm Dynamics: A Falsifiable, Pre-Registered Test | Computer Science Ai | 4.2 | 66% |
Opened Jul 12, 2026 · closed Jul 29, 2026