Under He/Lecun inits, theory implies Kernel OR Unstable regimes as width→∞. Discrepancies (e.g. feature learning) are seen as finite width effects. Our #NeurIPS2025 spotlight refutes this: practical nets do not converge to kernel limits; Feature learning persists as width→∞🧵

Stable model scaling with width-independent dynamics? Thrilled to present 2 papers at #NeurIPS 🎉 that study width-scaling in Sharpness Aware Minimization (SAM) (Th 16:30, #2104) and in Mamba (Fr 11, #7110). Our scaling rules stabilize training and transfer optimal hyperparams across scales. 🧵 1/10