Nobody ever reads the documentation :-/
The package vignette for feature selection had all the details. They can know be found at:
in Algorithm #2.
In your case, you have inner resampling to tune the SVM at each iteration (line 2.9 if Algo #2) and an external one to evaluate the number of predictors (line 2.1).
Why does it do this? With small to moderate numbers of instances, a simple partition to a single test set does a very poor job of estimating performance and may very well over-fit to the predictors.  concisely summarize this point: ``hold--out samples of tolerable size [...] do not match the cross--validation itself for reliability in assessing model fit and are hard to motivate''.
I would advise reading , which reflects how difficult validating feature selection can be. If you have a lot of data, perhaps a single test set would be sufficient.
One other note: you don't show what
svmFuncs is exactly, so I don't know how you are estimating variable importance. If you are using the default method, it does the analysis for each predictor independently so using
rerank = TRUE is a waste of time (i.e the values will be the same at each calculation).
 Hawkins, D. M., Basak, S. C., & Mills, D. (2003). Assessing Model Fit by Cross-Validation. Journal of Chemical Information and Modeling, 43(2), 579–586. doi:10.1021/ci025626i
 Ambroise, C., & McLachlan, G. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences, 99(10), 6562–6566.