Adaptive Resampling in Caret with Pre-specified Validation Set

I was wondering if this is the correct way to get adaptive sampling in caret working with a pre-specified validation set using index. I can get this to work using the 'cv' method in caret like so

data(iris)
library(caret)
library(dplyr)
library(gbm)
index_train = sample(nrow(iris), floor(nrow(iris) * .8))
index_list <- rep(list(index_train), 10)

iris <- select(iris, -Species)

ctrl <- trainControl(method = 'cv',
number = 1,
index = list(index_train)
)

gbm_fit <- caret::train(Sepal.Length ~ ., data = iris,
method = 'gbm',
verbose = TRUE, distribution = 'gaussian',
trControl = ctrl)


However, when I switch to adaptive_cv, this throws an error

> ctrl <- trainControl(method = 'adaptive_cv',
+                      number = 1,
+                      index = list(index_train)
+ )
Error in trainControl(method = "adaptive_cv", number = 1, index = index_list) :
adaptive\$min should be less than 1


Playing around with adaptive_min and number, it seems like adaptive_min has to be less than number but greater than 1. This means that I have to create artificial cross-folds even though I'm using the same train and validation holdout set. I do this by just repping my index vector 10x into a list (R will throw a subscript out of bounds error if you increase number and keep your index list the same).

index_list <- rep(list(index_train), 10)