The Conflict Between Logic and Memory: Training Conditions for Optimizer-Dependent Rule Acquisition
Optimizers can fit the same task while acquiring different generalizing relations. We study the training conditions governing these differences in single-hidden-layer ReLU networks, combining composite evidence tasks, parameter-level interventions, and a three-seed strict-parity scan. Our central finding is that nuisance-connected trainability reshapes both shared failures and relative optimizer advantages. In a nuisance-heavy task, all twenty tested optimizer configurations remain near chance on the hardest stage. Retaining every input but fixing nuisance-connected first-layer weights at initialization raises that stage's accuracy from approximately 50\% to 70.56\%, 68.47\%, and 69.00\% ...
| Authors | Gongyue Zhang, Honghai Liu |
|---|---|
| Primary category | cs.LG |
| Published | 30 September 2026 |
| Last updated at source | 2 October 2026 |
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