<ul><li>Transformer models have become essential in various scientific and engineering fields due to their exceptional performance.</li><li>Research has focused on comprehending the convergence dynamics of Transformers, especially concerning self-attention, feedforward networks, and residual connections.</li><li>The study reveals that proper initialization enables gradient descent to achieve a linear convergence rate influenced by the singular values of the attention layer output matrix.</li><li>Residual connections aid in improving optimization stability by mitigating the challenges arising from the low-rank structure imposed by softmax operation.</li></ul>

On the Convergence of Gradient Descent on Learning Transformers with Residual Connections

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