How do attention mechanisms reshape optimization dynamics in transformers?

Attention introduces dynamic computation graphs that alter gradient flow and loss conditioning.

Attention mechanisms dynamically weight token interactions, effectively changing the computational graph at each forward pass. This introduces adaptive feature selection but also increases gradient variance. The softmax normalization in attention can lead to sharp probability distributions, which affects stability and conditioning of the cost function.