Let's say you have 6 variables.
A Random-Forest regression using the first 5 variables has an R^2 of 0.1. Another regression using just the 6th variable yields R^2 of 0.3. All of the first 5 variables are uncorrelated with variable 6 (<0.1 correlation in absolute value).
Why would a regression with all 6 variables have an R^2 of 0.31 or 0.29, i.e. adding first 5 variables to the 6th variable only negligibly increases performance by 0.01 or even decreases it?
Notice that all models are tuned via random search cross-validation tuning depth, number of features, splits and number of trees.