Why does model converges faster with Label encoded data but very slowly with one hot encoding?

Quick context. I am trying to solve a very easy problem with self attention.

Let me provide the approaches I tried.

1. Training data was one hot encoded, then positional encoding was added. After that self attention followed by two feedforward layers.

2. Training data was label encoded, passed through an embedding layer, positional encoding was added. After that it was passed through self attention followed by two feedforward layers.

Now the results.
In the first approach, the training accuracy …

attention context data deeplearning easy encoding faster hot positional encoding solve training training data