Researchers found mechanisms in an in-context learning algorithm's weights that were analogous to gradient descent.

“then you do x and you expect the y and the neural network when you train it in this way actually does do does do linear regression and normally when you would run linear regression you have a small gradient descent optimizer that basically looks at x y looks at an error calculates the gradient of the weights and does the update a few times it just turns out that when they looked at the weights of that in context learning algorithm they actually found some”

“analogies to gradient descent mechanics.”