diff --git a/DESCRIPTION b/DESCRIPTION
index 9850451..0821294 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -5,7 +5,9 @@ Authors@R: c(
     person("Marc", "Becker", , "marcbecker@posteo.de", role = c("cre", "aut"),
            comment = c(ORCID = "0000-0002-8115-0400")),
     person("Michel", "Lang", , "michellang@gmail.com", role = "ctb",
-           comment = c(ORCID = "0000-0001-9754-0393"))
+           comment = c(ORCID = "0000-0001-9754-0393")),
+    person("Lukas", "Burk", , "github@quantenbrot.de", role = c("ctb"),
+           comment = c(ORCID = "0000-0001-7528-3795"))
   )
 Description: Collection of search spaces for hyperparameter optimization in the
   'mlr3' ecosystem. It features ready-to-use search spaces for many popular
diff --git a/R/TuningSpace.R b/R/TuningSpace.R
index 1eb787c..ebce9b7 100644
--- a/R/TuningSpace.R
+++ b/R/TuningSpace.R
@@ -144,14 +144,15 @@ TuningSpace = R6Class("TuningSpace",
     #' Printer.
     #'
     #' @param ... (ignored).
-    print = function(...) {
+     print = function(...) {
       tab = imap_dtr(self$values, function(value, name) {
         data.table(
             id = name,
-            lower = value$content$lower,
-            upper = value$content$upper,
-            levels = list(value$content$param$levels),
-            logscale = isTRUE(value$content$logscale)
+            lower = if (inherits(value, "TuneToken")) value$content$lower,
+            upper = if (inherits(value, "TuneToken")) value$content$upper,
+            levels = if (inherits(value, "TuneToken")) list(value$content$param$levels),
+            logscale = if (inherits(value, "TuneToken")) isTRUE(value$content$logscale),
+            constant = if (!inherits(value, "TuneToken")) value
           )
         }, .fill = TRUE)
       setcolorder(tab, c("id", "lower", "upper", "levels", "logscale"))
diff --git a/R/bibentries.R b/R/bibentries.R
index 7b25761..574072e 100644
--- a/R/bibentries.R
+++ b/R/bibentries.R
@@ -39,5 +39,16 @@ bibentries = c(
     journal = "arXiv",
     volume = "2106.11959",
     year = "2021",
+  ),
+  
+  burk_2024 = bibentry("misc",
+    key           = "burk_2024",
+    title         = "A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data",
+    author        = "Lukas Burk and John Zobolas and Bernd Bischl and Andreas Bender and Marvin N. Wright and Raphael Sonabend",
+    year          = "2024",
+    eprint        = "2406.04098",
+    archivePrefix = "arXiv",
+    primaryClass  = "stat.ML",
+    url           = "https://arxiv.org/abs/2406.04098"
   )
 )
\ No newline at end of file
diff --git a/R/tuning_spaces_sbb.R b/R/tuning_spaces_sbb.R
new file mode 100644
index 0000000..2569fd7
--- /dev/null
+++ b/R/tuning_spaces_sbb.R
@@ -0,0 +1,412 @@
+#' @title Burk (2024) Survival Tuning Spaces
+#'
+#' @name mlr_tuning_spaces_sbb
+#'
+#' @description
+#' Tuning spaces from the `r cite_bib("burk_2024")` article.
+#'
+#' @source
+#' `r format_bib("burk_2024")`
+#'
+#' @aliases
+#' mlr_tuning_spaces_surv.akritas.sbb
+#' mlr_tuning_spaces_surv.glmnet.sbb
+#' mlr_tuning_spaces_surv.penalized.sbb
+#' mlr_tuning_spaces_surv.parametric.sbb
+#' mlr_tuning_spaces_surv.flexsurv.sbb
+#' mlr_tuning_spaces_surv.rfsrc.sbb
+#' mlr_tuning_spaces_surv.ranger.sbb
+#' mlr_tuning_spaces_surv.cforest.sbb
+#' mlr_tuning_spaces_surv.aorsf.sbb
+#' mlr_tuning_spaces_surv.rpart.sbb
+#' mlr_tuning_spaces_surv.mboost.sbb
+#' mlr_tuning_spaces_surv.cv_coxboost.sbb
+#' mlr_tuning_spaces_surv.xgboost.cox.sbb
+#' mlr_tuning_spaces_surv.xgboost.aft.sbb
+#' mlr_tuning_spaces_surv.svm.sbb
+#'
+#' @section akritas tuning space:
+#' `r rd_info(lts("surv.akritas.sbb"))`
+#'
+#' @section cv_glmnet tuning space:
+#' `r rd_info(lts("surv.cv_glmnet.sbb"))`
+#'
+#' @section penalized tuning space:
+#' `r rd_info(lts("surv.penalized.sbb"))`
+#'
+#' @section parametric tuning space:
+#' `r rd_info(lts("surv.parametric.sbb"))`
+#'
+#' @section flexsurv tuning space:
+#' `r rd_info(lts("surv.flexible.sbb"))`
+#'
+#' @section rfsrc tuning space:
+#' `r rd_info(lts("surv.rfsrc.sbb"))`
+#'
+#' @section ranger tuning space:
+#' `r rd_info(lts("surv.ranger.sbb"))`
+#'
+#' @section cforest tuning space:
+#' `r rd_info(lts("surv.cforest.sbb"))`
+#'
+#' @section aorsf tuning space:
+#' `r rd_info(lts("surv.aorsf.sbb"))`
+#'
+#' @section rpart tuning space:
+#' `r rd_info(lts("surv.rpart.sbb"))`
+#'
+#' @section mboost tuning space:
+#' `r rd_info(lts("surv.mboost.sbb"))`
+#'
+#' @section cv_coxboost tuning space:
+#' `r rd_info(lts("surv.cv_coxboost.sbb"))`
+#'
+#' @section xgboost.cox tuning space:
+#' `r rd_info(lts("surv.xgboost.cox.sbb"))`
+#'
+#' @section xgboost.aft tuning space:
+#' `r rd_info(lts("surv.xgboost.aft.sbb"))`
+#'
+#' @section ssvm tuning space:
+#' `r rd_info(lts("surv.svm.sbb"))`
+#'
+#' @include mlr_tuning_spaces.R
+#'
+#' @examples
+#' if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
+#'   library(mlr3pipelines)
+#'   library(mlr3proba)
+#'
+#'   # Create survival learner ready to be tuned
+#'   aft = lts("surv.parametric.sbb")$get_learner()
+#'   aft
+#'
+#'   # Create survival graph learner ready to tuned
+#'   # get the tuning space of XGB-AFT learner
+#'   tuning_space = lts("surv.xgboost.aft.sbb")
+#'
+#'   # compose graph learner that can also predict survival distribution
+#'   xgb_aft = ppl("distrcompositor", learner = lrn("surv.xgboost.aft"),
+#'                 form = "aft", overwrite = TRUE, graph_learner = TRUE)
+#'
+#'   # set tuning parameters
+#'   xgb_aft$graph$pipeops$surv.xgboost.aft$param_set$set_values(.values = tuning_space$values)
+#'
+#'   # graph learner is ready to be tuned!
+#'   xgb_aft
+#'
+#'   # Another way (more concise):
+#'   xgb_aft = ppl("distrcompositor",
+#'                 learner = lts("surv.xgboost.aft.sbb")$get_learner(),
+#'                 form = "aft", overwrite = TRUE, graph_learner = TRUE)
+#'   xgb_aft
+#'
+#' }
+NULL
+
+# Akritas -------------------------------------------------------------------------------------
+
+
+# survivalmodels::akritas
+# https://raphaels1.github.io/survivalmodels/reference/akritas.html
+vals = list(
+  lambda = to_tune(0, 1)
+)
+
+add_tuning_space(
+  id = "surv.akritas.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.akritas",
+  package = "mlr3extralearners",
+  label = "Akritas Estimator (SBB)"
+)
+
+
+# glmnet --------------------------------------------------------------------------------------
+
+vals = list(
+  alpha = to_tune(0, 1)
+)
+
+add_tuning_space(
+  id = "surv.cv_glmnet.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.cv_glmnet",
+  package = "mlr3extralearners",
+  label = "Survival CV Elastic Net (SBB)"
+)
+
+
+# Penalized -----------------------------------------------------------------------------------
+
+vals = list(
+  lambda1 = to_tune(p_dbl(-10, 10, trafo = function(x) 2^x)),
+  lambda2 = to_tune(p_dbl(-10, 10, trafo = function(x) 2^x))
+)
+
+add_tuning_space(
+  id = "surv.penalized.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.penalized",
+  package = "mlr3extralearners",
+  label = "Survival Penalized Regression (SBB)"
+)
+
+
+# Parametric / AFT ----------------------------------------------------------------------------
+
+
+# Use grid search due to small + finite search space
+# AFT version needs
+# - to pass .form to bl() for distrcompositor
+# - Tune distributions within range of what's sensible/discussed with RS
+vals = list(
+  form = "aft",
+  # discrete = TRUE, # required at predict time, not sure if needed here?
+  dist = to_tune(c("weibull", "exponential", "lognormal",  "loglogistic"))
+)
+
+add_tuning_space(
+  id = "surv.parametric.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.parametric",
+  package = "mlr3extralearners",
+  label = "Parametric AFT (SBB)"
+)
+
+
+# FlexSurv ------------------------------------------------------------------------------------
+
+# Use grid search due to small + finite search space
+vals = list(
+  k = to_tune(p_int(1, 10))
+)
+
+add_tuning_space(
+  id = "surv.flexible.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.flexible",
+  package = "mlr3extralearners",
+  label = "Survival Flexible Parametric Splines (SBB)"
+)
+
+
+# RFSRC ---------------------------------------------------------------------------------------
+
+vals = list(
+  # Fixing ntime = 150 (current default) just to be explicit, as ranger's time.interest
+  # is set to a non-default value and we ensure both use 150 time points for evaluation
+  ntree = 1000,
+  ntime = 150,
+  splitrule = to_tune(c("bs.gradient", "logrank")),
+  mtry.ratio = to_tune(0, 1),
+  nodesize = to_tune(p_int(1, 50)),
+  samptype = to_tune(c("swr", "swor")),
+  sampsize.ratio = to_tune(0, 1)
+)
+
+add_tuning_space(
+  id = "surv.rfsrc.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.rfsrc",
+  package = "mlr3extralearners",
+  label = "RSF (rfsrc) (SBB)"
+)
+
+
+# ranger' -------------------------------------------------------------------------------------
+
+vals = list(
+  # Adjusting time.interest (new as of 0.16.0) to 150, same as current RFSRC default
+  num.trees = 1000,
+  time.interest = 150,
+  splitrule = to_tune(c("C", "maxstat", "logrank")),
+  mtry.ratio = to_tune(0, 1),
+  min.node.size = to_tune(p_int(1, 50)),
+  replace = to_tune(),
+  sample.fraction = to_tune(0, 1)
+)
+
+add_tuning_space(
+  id = "surv.ranger.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.ranger",
+  package = "mlr3extralearners",
+  label = "RSF (ranger) (SBB)"
+)
+
+
+# CIF -----------------------------------------------------------------------------------------
+
+
+vals = list(
+  ntree = 1000,
+  mtryratio = to_tune(0, 1),
+  minsplit = to_tune(p_int(1, 50)),
+  mincriterion = to_tune(0, 1),
+  replace = to_tune(p_lgl()),
+  fraction = to_tune(0, 1)
+)
+
+add_tuning_space(
+  id = "surv.cforest.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.cforest",
+  package = "mlr3extralearners",
+  label = "Conditional Inference RSF (SBB)"
+)
+
+
+# aorsf ---------------------------------------------------------------------------------------
+
+vals = list(
+  n_tree = 1000,
+  control_type = "fast",
+  mtry_ratio = to_tune(0, 1),
+  leaf_min_events = to_tune(p_int(5, 50)),
+  .extra_trafo = function(x, param_set) {
+    x$split_min_obs = x$leaf_min_events + 5L
+    x
+  }
+)
+
+add_tuning_space(
+  id = "surv.aorsf.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.aorsf",
+  package = "mlr3extralearners",
+  label = "Oblique RSF (SBB)"
+)
+
+
+# rpart RRT -----------------------------------------------------------------------------------
+
+vals = list(
+  minbucket = to_tune(p_int(5, 50))
+)
+
+add_tuning_space(
+  id = "surv.rpart.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.rpart",
+  package = "mlr3extralearners",
+  label = "Relative Risk Tree (SBB)"
+)
+
+# mboost --------------------------------------------------------------------------------------
+
+vals = list(
+  family = to_tune(c("gehan", "cindex", "coxph", "weibull")),
+  mstop = to_tune(p_int(10, 5000)),
+  nu = to_tune(0, 0.1),
+  baselearner = to_tune(c("bols", "btree"))
+)
+
+add_tuning_space(
+  id = "surv.mboost.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.mboost",
+  package = "mlr3extralearners",
+  label = "Model-Based Boosting (SBB)"
+)
+
+# CoxBoost ------------------------------------------------------------------------------------
+# We don't tune this explicitly but let it tune itself
+
+vals = list(
+  penalty = "optimCoxBoostPenalty",
+  maxstepno = 5000
+)
+
+add_tuning_space(
+  id = "cv_coxboost.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "cv_coxboost",
+  package = "mlr3extralearners",
+  label = "CV CoxBoost (SBB)"
+)
+
+
+# XGB Cox -------------------------------------------------------------------------------------
+
+vals = list(
+  tree_method = "hist",
+  booster = "gbtree",
+  max_depth = to_tune(p_int(1, 20)),
+  subsample = to_tune(0, 1),
+  colsample_bytree = to_tune(0, 1),
+  nrounds = to_tune(p_int(10, 5000)),
+  eta = to_tune(0, 1),
+  grow_policy = to_tune(c("depthwise", "lossguide"))
+)
+
+add_tuning_space(
+  id = "surv.xgboost.cox.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.xgboost.cox",
+  package = "mlr3extralearners",
+  label = "Survival XGBoost (Cox) (SBB)"
+)
+
+# XGB AFT -------------------------------------------------------------------------------------
+
+vals = list(
+  tree_method = "hist",
+  booster = "gbtree",
+  max_depth = to_tune(p_int(1, 20)),
+  subsample = to_tune(0, 1),
+  colsample_bytree = to_tune(0, 1),
+  nrounds = to_tune(p_int(10, 5000)),
+  eta = to_tune(0, 1),
+  grow_policy = to_tune(c("depthwise", "lossguide")),
+  aft_loss_distribution = to_tune(c("normal", "logistic", "extreme")),
+  aft_loss_distribution_scale = to_tune(0.5, 2.0)
+)
+
+add_tuning_space(
+  id = "surv.xgboost.aft.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.xgboost.aft",
+  package = "mlr3extralearners",
+  label = "Survival XGBoost (AFT) (SBB)"
+)
+
+# SSVM ----------------------------------------------------------------------------------------
+
+vals = list(
+  type = "hybrid",
+  gamma.mu = 0,
+  diff.meth = "makediff3",
+  kernel = to_tune(p_fct(c("lin_kernel", "rbf_kernel", "add_kernel"))),
+  gamma = to_tune(p_dbl(-10, 10, trafo = function(x) 10^x)),
+  mu = to_tune(p_dbl(-10, 10, trafo = function(x) 10^x)),
+  kernel.pars = to_tune(p_dbl(-5, 5, trafo = function(x) 2^x)),
+  .extra_trafo = function(x, param_set) {
+    x$gamma.mu = c(x$gamma, x$mu)
+    x$gamma = x$mu = NULL
+    x
+  }
+)
+
+add_tuning_space(
+  id = "surv.svm.sbb",
+  values = vals,
+  tags = c("sbb", "survival"),
+  learner = "surv.svm",
+  package = "mlr3extralearners",
+  label = "Survival SVM (SBB)"
+)
diff --git a/man/mlr3tuningspaces-package.Rd b/man/mlr3tuningspaces-package.Rd
index d0e5129..25686c4 100644
--- a/man/mlr3tuningspaces-package.Rd
+++ b/man/mlr3tuningspaces-package.Rd
@@ -25,6 +25,7 @@ Useful links:
 Other contributors:
 \itemize{
   \item Michel Lang \email{michellang@gmail.com} (\href{https://orcid.org/0000-0001-9754-0393}{ORCID}) [contributor]
+  \item Lukas Burk \email{github@quantenbrot.de} (\href{https://orcid.org/0000-0001-7528-3795}{ORCID}) [contributor]
 }
 
 }
diff --git a/man/mlr_tuning_spaces_sbb.Rd b/man/mlr_tuning_spaces_sbb.Rd
new file mode 100644
index 0000000..5873869
--- /dev/null
+++ b/man/mlr_tuning_spaces_sbb.Rd
@@ -0,0 +1,119 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tuning_spaces_sbb.R
+\name{mlr_tuning_spaces_sbb}
+\alias{mlr_tuning_spaces_sbb}
+\alias{mlr_tuning_spaces_surv.akritas.sbb}
+\alias{mlr_tuning_spaces_surv.glmnet.sbb}
+\alias{mlr_tuning_spaces_surv.penalized.sbb}
+\alias{mlr_tuning_spaces_surv.parametric.sbb}
+\alias{mlr_tuning_spaces_surv.flexsurv.sbb}
+\alias{mlr_tuning_spaces_surv.rfsrc.sbb}
+\alias{mlr_tuning_spaces_surv.ranger.sbb}
+\alias{mlr_tuning_spaces_surv.cforest.sbb}
+\alias{mlr_tuning_spaces_surv.aorsf.sbb}
+\alias{mlr_tuning_spaces_surv.rpart.sbb}
+\alias{mlr_tuning_spaces_surv.mboost.sbb}
+\alias{mlr_tuning_spaces_surv.cv_coxboost.sbb}
+\alias{mlr_tuning_spaces_surv.xgboost.cox.sbb}
+\alias{mlr_tuning_spaces_surv.xgboost.aft.sbb}
+\alias{mlr_tuning_spaces_surv.svm.sbb}
+\title{Burk (2024) Survival Tuning Spaces}
+\source{
+Burk L, Zobolas J, Bischl B, Bender A, Wright MN, Sonabend R (2024).
+\dQuote{A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data.}
+2406.04098, \url{https://arxiv.org/abs/2406.04098}.
+}
+\description{
+Tuning spaces from the Burk (2024) article.
+}
+\section{akritas tuning space}{
+
+\itemize{
+\item lambda \eqn{[0, 1]}{[0, 1]}
+}
+}
+
+\section{cv_glmnet tuning space}{
+
+\itemize{
+\item alpha \eqn{[0, 1]}{[0, 1]}
+}
+}
+
+\section{penalized tuning space}{
+
+\itemize{
+\item lambda1 \eqn{[-10, 10]}{[-10, 10]} (2^x)
+\item lambda2 \eqn{[-10, 10]}{[-10, 10]} (2^x)
+}
+}
+
+\section{parametric tuning space}{
+
+\verb{r rd_info(lts("surv.parametric.sbb"))}
+}
+
+\section{flexsurv tuning space}{
+
+\itemize{
+\item k \eqn{[1, 10]}{[1, 10]}
+}
+}
+
+\section{rfsrc tuning space}{
+
+\verb{r rd_info(lts("surv.rfsrc.sbb"))}
+}
+
+\section{ranger tuning space}{
+
+\verb{r rd_info(lts("surv.ranger.sbb"))}
+}
+
+\section{cforest tuning space}{
+
+\verb{r rd_info(lts("surv.cforest.sbb"))}
+}
+
+\section{aorsf tuning space}{
+
+\verb{r rd_info(lts("surv.aorsf.sbb"))}
+}
+
+\section{rpart tuning space}{
+
+\itemize{
+\item minbucket \eqn{[5, 50]}{[5, 50]}
+}
+}
+
+\section{mboost tuning space}{
+
+\itemize{
+\item family [\dQuote{gehan}, \dQuote{cindex}, \dQuote{coxph}, \dQuote{weibull}]
+\item mstop \eqn{[10, 5000]}{[10, 5000]}
+\item nu \eqn{[0, 0.1]}{[0, 0.1]}
+\item baselearner [\dQuote{bols}, \dQuote{btree}]
+}
+}
+
+\section{cv_coxboost tuning space}{
+
+\verb{r rd_info(lts("surv.cv_coxboost.sbb"))}
+}
+
+\section{xgboost.cox tuning space}{
+
+\verb{r rd_info(lts("surv.xgboost.cox.sbb"))}
+}
+
+\section{xgboost.aft tuning space}{
+
+\verb{r rd_info(lts("surv.xgboost.aft.sbb"))}
+}
+
+\section{ssvm tuning space}{
+
+\verb{r rd_info(lts("surv.svm.sbb"))}
+}
+