I think the idea is that gbt's are trained sequentially on the "residuals" left from the previous tree, while rf's are uncorrelated (can be trained in parallel,

I think the idea is that gbt's are trained sequentially on the "residuals" left from the previous tree, while rf's are uncorrelated (can be trained in parallel, don't depend on one another). This means that in rf's there's no "compounding" effect that can lead to overfitting. The main way to overfit rf's would be to train many large trees.