Anti-grokking: overtraining can collapse generalization, driven by "Correlation Traps"
CatAstro_Piyush · x · 2026-09-26
Researchers identify "anti-grokking": if you keep training past the grokking peak, test accuracy can suddenly collapse back to chance even while training accuracy stays perfect. Standard progress metrics (L2 weight norms, activation sparsity, weight entropy) track the initial grokking phase but stay flat and miss the collapse. Using the WeightWatcher tool to analyze the empirical spectral density of layer weight matrices, the authors show anti-grokking is driven by "Correlation Traps" — anomalously large eigenvalues emerging late in training that severely impair generalization.
More from Research
- Reprogramming fewer than 10 dormant neurons backdoors any LLM agent, no training needed — DimitrisPapail · 2026-11-11
- Scandinavian Journal of Statistics editors reflect on publishing in the age of generative AI — michaelchchoi · 2026-11-11
- Proving LLM backdoor-safety without revealing weights: ZKML over formal methods — DimitrisPapail · 2026-11-11
- LLMs can backdoor other LLMs by reprogramming a few dormant neurons to trigger rm -rf — DimitrisPapail · 2026-11-11
- Claude Opus 5.5 runs an architecture search and finds AttnRes-Canon, a new top Transformer design — SonglinYang4 · 2026-11-11
- 5 research habits worth knowing: read papers backwards, reproduce first, wire coding agents via MCP — cneuralnetwork · 2026-11-11