AINeutralarXiv – CS AI · 8h ago6/10
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Inverse Depth Scaling From Most Layers Being Similar
Researchers analyzing large language models find that loss scales inversely with network depth, suggesting most layers function similarly and reduce error through ensemble averaging rather than compositional learning. This inefficient scaling pattern may stem from architectural constraints in residual networks, indicating that improving LLM efficiency requires fundamental architectural innovations rather than simply adding more layers.