Most nets use He/Lecun init with single LR η. As width m→∞, theory says
η∈O(1/m)⟹Kernel; η∈ω(1/m)⟹Unstable.
Thus max stable LR∝1/m.
Practice violates this. Optimal LRs are larger (e.g.∝1/√m) & models admit feature learning; contradicts kernel predictions. Why? (2/10)
