diff --git a/R/resample.R b/R/resample.R
index cc1bb88f2..ea75b228e 100644
--- a/R/resample.R
+++ b/R/resample.R
@@ -15,6 +15,7 @@
 #' @template param_allow_hotstart
 #' @template param_clone
 #' @template param_unmarshal
+#' @template param_extractor
 #' @return [ResampleResult].
 #'
 #' @template section_predict_sets
@@ -55,7 +56,18 @@
 #' bmr1 = as_benchmark_result(rr)
 #' bmr2 = as_benchmark_result(rr_featureless)
 #' print(bmr1$combine(bmr2))
-resample = function(task, learner, resampling, store_models = FALSE, store_backends = TRUE, encapsulate = NA_character_, allow_hotstart = FALSE, clone = c("task", "learner", "resampling"), unmarshal = TRUE) {
+resample = function(
+  task,
+  learner,
+  resampling,
+  store_models = FALSE,
+  store_backends = TRUE,
+  encapsulate = NA_character_,
+  allow_hotstart = FALSE,
+  clone = c("task", "learner", "resampling"),
+  unmarshal = TRUE,
+  extractor = NULL
+  ) {
   assert_subset(clone, c("task", "learner", "resampling"))
   task = assert_task(as_task(task, clone = "task" %in% clone))
   learner = assert_learner(as_learner(learner, clone = "learner" %in% clone, discard_state = TRUE))
@@ -115,7 +127,7 @@ resample = function(task, learner, resampling, store_models = FALSE, store_backe
   }
 
   res = future_map(n, workhorse, iteration = seq_len(n), learner = grid$learner, mode = grid$mode,
-    MoreArgs = list(task = task, resampling = resampling, store_models = store_models, lgr_threshold = lgr_threshold, pb = pb, unmarshal = unmarshal)
+    MoreArgs = list(task = task, resampling = resampling, store_models = store_models, lgr_threshold = lgr_threshold, pb = pb, unmarshal = unmarshal, extractor = extractor)
   )
 
   data = data.table(
diff --git a/R/worker.R b/R/worker.R
index e52daa23f..0cf16610b 100644
--- a/R/worker.R
+++ b/R/worker.R
@@ -251,7 +251,20 @@ learner_predict = function(learner, task, row_ids = NULL) {
 }
 
 
-workhorse = function(iteration, task, learner, resampling, param_values = NULL, lgr_threshold, store_models = FALSE, pb = NULL, mode = "train", is_sequential = TRUE, unmarshal = TRUE) {
+workhorse = function(
+  iteration,
+  task,
+  learner,
+  resampling,
+  param_values = NULL,
+  lgr_threshold,
+  store_models = FALSE,
+  pb = NULL,
+  mode = "train",
+  is_sequential = TRUE,
+  unmarshal = TRUE,
+  extractor = NULL
+  ) {
   if (!is.null(pb)) {
     pb(sprintf("%s|%s|i:%i", task$id, learner$id, iteration))
   }
@@ -332,6 +345,10 @@ workhorse = function(iteration, task, learner, resampling, param_values = NULL,
   }
   pdatas = discard(pdatas, is.null)
 
+  if (!is.null(extractor)) {
+    learner$state = insert_named(learner$state, extractor(learner$model))
+  }
+
   # set the model slot after prediction so it can be sent back to the main process
   process_model_after_predict(
     learner = learner, store_models = store_models, is_sequential = is_sequential, model_copy = model_copy_or_null,
diff --git a/man-roxygen/param_extractor.R b/man-roxygen/param_extractor.R
new file mode 100644
index 000000000..846298bc1
--- /dev/null
+++ b/man-roxygen/param_extractor.R
@@ -0,0 +1,3 @@
+#' @param extractor (`function()`)\cr
+#' Function to extract information from the learner model on the worker.
+#' The function takes `model` as input and must return a named list.
diff --git a/man/Task.Rd b/man/Task.Rd
index f45a41ddc..4e925c1ad 100644
--- a/man/Task.Rd
+++ b/man/Task.Rd
@@ -147,9 +147,6 @@ Required for \code{\link[=convert_task]{convert_task()}}.}
 
 \item{\code{mlr3_version}}{(\code{package_version})\cr
 Package version of \code{mlr3} used to create the task.}
-
-\item{\code{characteristics}}{(\code{list()})\cr
-List of characteristics of the task, e.g. \code{list(n = 5, p = 7)}.}
 }
 \if{html}{\out{</div>}}
 }
@@ -303,6 +300,9 @@ Alternatively, you can provide a \code{\link[=data.frame]{data.frame()}} with th
 Hash (unique identifier) for all columns except the \code{primary_key}: A \code{character} vector, named by the columns that each element refers to.\cr
 Columns of different \code{\link{Task}}s or \code{\link{DataBackend}}s that have agreeing \code{col_hashes} always represent the same data, given that the same \code{row}s are selected.
 The reverse is not necessarily true: There can be columns with the same content that have different \code{col_hashes}.}
+
+\item{\code{characteristics}}{(\code{list()})\cr
+List of characteristics of the task, e.g. \code{list(n = 5, p = 7)}.}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/resample.Rd b/man/resample.Rd
index b972108ef..e1c895822 100644
--- a/man/resample.Rd
+++ b/man/resample.Rd
@@ -13,7 +13,8 @@ resample(
   encapsulate = NA_character_,
   allow_hotstart = FALSE,
   clone = c("task", "learner", "resampling"),
-  unmarshal = TRUE
+  unmarshal = TRUE,
+  extractor = NULL
 )
 }
 \arguments{
@@ -64,6 +65,10 @@ Per default, all input objects are cloned.}
 Whether to unmarshal learners that were marshaled during the execution.
 If \code{TRUE} all models are stored in unmarshaled form.
 If \code{FALSE}, all learners (that need marshaling) are stored in marshaled form.}
+
+\item{extractor}{(\verb{function()})\cr
+Function to extract information from the learner model on the worker.
+The function takes \code{model} as input and must return a named list.}
 }
 \value{
 \link{ResampleResult}.