# What parameter of GBM does gradient descent update after calculating gradient of loss function?

I am going through Elements of statistical Learning and trying to understand GBM algorithm.

The algorithm of GBM is shown below.

I understand general gradient descent algorithm mentioned below very well.

Questions

1. Which parameter (theta j in the above picture) of GBM is gradient descent updating using each new tree that is added to GBM? Can you explain the above GBM algorithm intuitive in this context?
2. What is the gamma in the GBM algorithm and intuition behind it?
3. Seems gamma is calculated for each terminal region per each tree. What does it mean/do?
4. GBM does not use reweighing of training samples unlike Adaboost which does. True or False?