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Transformer training is notoriously difficult, requiring a careful design ofoptimizers and use of various heuristics. We make progress towardsunderstanding the subtleties of training transformers by carefully studying asimple yet canonical linearized shallow transformer model. Specifically, wetrain linear transformers to solve regression tasks, inspired by J. von Oswaldet al. (ICML 2023), and K. Ahn et al. (NeurIPS 2023). Most importantly, weobserve that our proposed linearized models can reproduce several prominentaspects of transformer training dynamics. Consequently, the results obtained inthis paper suggest that a simple linearized transformer model could actually bea valuable, realistic abstraction for understanding transformer optimization.

Linear attention is (maybe) all you need (to understand transformer optimization)