diff --git a/R/MeasureRegrRSQ.R b/R/MeasureRegrRSQ.R
index 8019b1d90..5fbd1e2e4 100644
--- a/R/MeasureRegrRSQ.R
+++ b/R/MeasureRegrRSQ.R
@@ -29,7 +29,7 @@
 #' @template seealso_measure
 #' @export
 MeasureRegrRSQ = R6Class("MeasureRSQ",
-  inherit = Measure,
+  inherit = MeasureRegr,
   public = list(
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
@@ -40,10 +40,10 @@ MeasureRegrRSQ = R6Class("MeasureRSQ",
 
       super$initialize(
         id = "rsq",
-        task_type = "regr",
         properties = if (!private$.pred_set_mean) c("requires_task", "requires_train_set") else character(0),
         predict_type = "response",
         minimize = FALSE,
+        range = c(-Inf, 1),
         man = "mlr3::mlr_measures_regr.rsq"
       )
     }
diff --git a/R/as_prediction.R b/R/as_prediction.R
index a5ab76a90..0b87e1b1b 100644
--- a/R/as_prediction.R
+++ b/R/as_prediction.R
@@ -8,6 +8,8 @@
 #' @return [Prediction].
 #' @export
 as_prediction = function(x, check = FALSE, ...) {
+  if (is.null(x)) return(list())
+
   UseMethod("as_prediction")
 }
 
diff --git a/R/worker.R b/R/worker.R
index 3ee363ebc..f147adbeb 100644
--- a/R/worker.R
+++ b/R/worker.R
@@ -326,6 +326,10 @@ workhorse = function(iteration, task, learner, resampling, param_values = NULL,
     lg$debug("Creating Prediction for predict set '%s'", set)
     learner_predict(learner, task, row_ids)
   }, set = predict_sets, row_ids = pred_data$sets, task = pred_data$tasks)
+
+  if (!length(predict_sets)) {
+    learner$state$predict_time = 0L
+  }
   pdatas = discard(pdatas, is.null)
 
   # set the model slot after prediction so it can be sent back to the main process
diff --git a/man/mlr_measures_regr.rsq.Rd b/man/mlr_measures_regr.rsq.Rd
index 8798a5594..5cfb3295a 100644
--- a/man/mlr_measures_regr.rsq.Rd
+++ b/man/mlr_measures_regr.rsq.Rd
@@ -33,7 +33,7 @@ msr("regr.rsq")
 
 \itemize{
 \item Task type: \dQuote{regr}
-\item Range: \eqn{(-\infty, \infty)}{(-Inf, Inf)}
+\item Range: \eqn{(-\infty, 1]}{(-Inf, 1]}
 \item Minimize: FALSE
 \item Average: macro
 \item Required Prediction: \dQuote{response}
@@ -76,8 +76,8 @@ Other Measure:
 \code{\link{mlr_measures_selected_features}}
 }
 \concept{Measure}
-\section{Super class}{
-\code{\link[mlr3:Measure]{mlr3::Measure}} -> \code{MeasureRSQ}
+\section{Super classes}{
+\code{\link[mlr3:Measure]{mlr3::Measure}} -> \code{\link[mlr3:MeasureRegr]{mlr3::MeasureRegr}} -> \code{MeasureRSQ}
 }
 \section{Methods}{
 \subsection{Public methods}{
diff --git a/tests/testthat/test_benchmark.R b/tests/testthat/test_benchmark.R
index 6e73c799e..e83c9dcbf 100644
--- a/tests/testthat/test_benchmark.R
+++ b/tests/testthat/test_benchmark.R
@@ -567,3 +567,16 @@ test_that("predictions retrieved with as.data.table and predictions method are e
   predictions = unlist(map(bmr$resample_results$resample_result, function(rr) rr$predictions(predict_sets = "train")), recursive = FALSE)
   expect_equal(tab$prediction, predictions)
 })
+
+test_that("score works with predictions and empty predictions", {
+  learner_1 = lrn("classif.rpart", predict_sets = "train", id = "learner_1")
+  learner_2 = lrn("classif.rpart", predict_sets = "test", id = "learner_2")
+  task = tsk("pima")
+
+  design = benchmark_grid(task, list(learner_1, learner_2), rsmp("holdout"))
+
+  bmr = benchmark(design)
+
+  expect_warning({tab = bmr$score(msr("classif.ce", predict_sets = "test"))}, "Measure")
+  expect_equal(tab$classif.ce[1], NaN)
+})
diff --git a/tests/testthat/test_resample.R b/tests/testthat/test_resample.R
index 35a2a5afa..9896f8b48 100644
--- a/tests/testthat/test_resample.R
+++ b/tests/testthat/test_resample.R
@@ -473,6 +473,9 @@ test_that("resample result works with not predicted predict set", {
   tab = as.data.table(rr)
   expect_list(tab$prediction, len = 1)
   expect_list(tab$prediction[[1]], len = 0)
+
+  expect_warning({tab = rr$score(msr("classif.ce", predict_sets = "test"))}, "Measure")
+  expect_equal(tab$classif.ce, NaN)
 })
 
 test_that("resample results works with no predicted predict set", {
@@ -489,4 +492,14 @@ test_that("resample results works with no predicted predict set", {
   tab = as.data.table(rr)
   expect_list(tab$prediction, len = 1)
   expect_list(tab$prediction[[1]], len = 0)
+
+  expect_warning({tab = rr$score(msr("classif.ce", predict_sets = "test"))}, "Measure")
+  expect_equal(tab$classif.ce, NaN)
+})
+
+test_that("predict_time is 0 if no predict_set is specified", {
+  learner = lrn("classif.featureless", predict_sets = NULL)
+  rr = resample(task, learner, resampling)
+  times = rr$score(msr("time_predict"))$time_predict
+  expect_true(all(times == 0))
 })