Comment by nilirl
I wouldn't jump to call it a trick, but I agree, the author sacrificed too much clarity in a try for efficiency.
The author set up an interesting analogy but failed to explore where it breaks down or how all the relationships work in the model.
My inference about the author's meaning was such: In a sharp peak, searching for useful moves is harder because you have fewer acceptable options as you approach the peak.
Fewer absolute or relative? If you scale down your search space... This only makes some kind of sense if your step size is fixed. While I agree with another poster that a reduction of a creative process to gradient descent is not wise, the article also misses the point what makes such a gradient descent hard -- it's not sharp peaks, it's the flat area around them -- and the presence of local minima.