diff --git a/DESCRIPTION b/DESCRIPTION
index 306981847..a469f1218 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: tune
 Title: Tidy Tuning Tools
-Version: 1.1.1
+Version: 1.1.2
 Authors@R: c(
     person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-2402-136X")),
@@ -31,7 +31,7 @@ Imports:
     purrr (>= 1.0.0),
     recipes (>= 1.0.4),
     rlang (>= 1.0.2),
-    rsample (>= 1.0.0),
+    rsample (>= 1.1.1.9001),
     tibble (>= 3.1.0),
     tidyr (>= 1.2.0),
     tidyselect (>= 1.1.2),
diff --git a/NEWS.md b/NEWS.md
index c20cf1e66..67015951e 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,9 @@
+# tune 1.1.2
+
+* `last_fit()` now works with the 3-way validation split objects from `rsample::initial_validation_split()`. `last_fit()` and `fit_best()` now have a new argument `add_validation_set` to include or exclude the validation set in the dataset used to fit the model (#701).
+
+* Disambiguates the `verbose` and `verbose_iter` control options to better align with documented functionality. The former controls logging for general progress updates, while the latter only does so for the Bayesian search process. (#682)
+
 # tune 1.1.1
 
 * Fixed a bug introduced in tune 1.1.0 in `collect_()` functions where the 
diff --git a/R/fit_best.R b/R/fit_best.R
index e1951abef..79468bc87 100644
--- a/R/fit_best.R
+++ b/R/fit_best.R
@@ -14,6 +14,12 @@
 #' If `NULL`, this argument will be set to
 #' [`select_best(metric)`][tune::select_best.tune_results].
 #' @param verbose A logical for printing logging.
+#' @param add_validation_set When the resamples embedded in `x` are a split into
+#' training set and validation set, should the validation set be included in the
+#' data set used to train the model? If not, only the training set is used. If
+#' `NULL`, the validation set is not used for resamples originating from
+#' [rsample::validation_set()] while it is used for resamples originating
+#' from [rsample::validation_split()].
 #' @param ... Not currently used.
 #' @details
 #' This function is a shortcut for the manual steps of:
@@ -24,10 +30,6 @@
 #'   wflow_fit <- fit(wflow, data_set)
 #' }
 #'
-#' The data used for the fit are taken from the `splits` column. If the split
-#' column was from a validation split, the combined training and validation sets
-#' are used.
-#'
 #' In comparison to [last_fit()], that function requires a finalized model, fits
 #' the model on the training set defined by [rsample::initial_split()], and
 #' computes metrics from the test set.
@@ -85,6 +87,7 @@ fit_best.tune_results <- function(x,
                                   metric = NULL,
                                   parameters = NULL,
                                   verbose = FALSE,
+                                  add_validation_set = NULL,
                                   ...) {
   if (length(list(...))) {
     cli::cli_abort(c("x" = "The `...` are not used by this function."))
@@ -121,7 +124,29 @@ fit_best.tune_results <- function(x,
 
   # ----------------------------------------------------------------------------
 
-  dat <- x$splits[[1]]$data
+  if (inherits(x$splits[[1]], "val_split")) {
+    if (is.null(add_validation_set)) {
+      rset_info <- attr(x, "rset_info")
+      originate_from_3way_split <- rset_info$att$origin_3way %||% FALSE
+      if (originate_from_3way_split) {
+        add_validation_set <- FALSE
+      } else {
+        add_validation_set <- TRUE
+      }
+    }
+    if (add_validation_set) {
+      dat <- x$splits[[1]]$data
+    } else {
+      dat <- rsample::training(x$splits[[1]])
+    }
+  } else {
+    if (!is.null(add_validation_set)) {
+      rlang::warn(
+        "The option `add_validation_set` is being ignored because the resampling object does not include a validation set."
+      )
+    }
+    dat <- x$splits[[1]]$data
+  }
   if (verbose) {
     cli::cli_inform(c("i" = "Fitting using {nrow(dat)} data points..."))
   }
diff --git a/R/grid_performance.R b/R/grid_performance.R
index 8d73dfa58..a0f600e35 100644
--- a/R/grid_performance.R
+++ b/R/grid_performance.R
@@ -48,6 +48,10 @@ metrics_info <- function(x) {
 #' @param new_data A data frame or matrix of predictors to process.
 #' @param metrics_info The output of `tune:::metrics_info(metrics)`---only
 #' included as an argument to allow for pre-computing.
+#' @param catalog A logical passed to `tune_log()` giving whether the message
+#' is compatible with the issue cataloger. Defaults to `TRUE`. Updates that are
+#' always unique and do not represent a tuning "issue" can bypass the cataloger
+#' by setting `catalog = FALSE`.
 #' @keywords internal
 #' @name tune-internal-functions
 #' @export
diff --git a/R/last_fit.R b/R/last_fit.R
index 910599367..709496e32 100644
--- a/R/last_fit.R
+++ b/R/last_fit.R
@@ -10,7 +10,8 @@
 #' @param preprocessor A traditional model formula or a recipe created using
 #'   [recipes::recipe()].
 #'
-#' @param split An `rsplit` object created from [rsample::initial_split()].
+#' @param split An `rsplit` object created from [rsample::initial_split()] or
+#' [rsample::initial_validation_split()].
 #'
 #' @param metrics A [yardstick::metric_set()], or `NULL` to compute a standard
 #'   set of metrics.
@@ -18,6 +19,11 @@
 #' @param control A [control_last_fit()] object used to fine tune the last fit
 #'   process.
 #'
+#' @param add_validation_set For 3-way splits into training, validation, and test
+#' set via [rsample::initial_validation_split()], should the validation set be
+#' included in the data set used to train the model. If not, only the training
+#' set is used.
+#'
 #' @param ... Currently unused.
 #'
 #' @details
@@ -73,7 +79,9 @@ last_fit.default <- function(object, ...) {
 
 #' @export
 #' @rdname last_fit
-last_fit.model_spec <- function(object, preprocessor, split, ..., metrics = NULL, control = control_last_fit()) {
+last_fit.model_spec <- function(object, preprocessor, split, ..., metrics = NULL,
+                                control = control_last_fit(),
+                                add_validation_set = FALSE) {
   if (rlang::is_missing(preprocessor) || !is_preprocessor(preprocessor)) {
     rlang::abort(paste(
       "To tune a model spec, you must preprocess",
@@ -93,22 +101,27 @@ last_fit.model_spec <- function(object, preprocessor, split, ..., metrics = NULL
     wflow <- add_formula(wflow, preprocessor)
   }
 
-  last_fit_workflow(wflow, split, metrics, control)
+  last_fit_workflow(wflow, split, metrics, control, add_validation_set)
 }
 
 
 #' @rdname last_fit
 #' @export
-last_fit.workflow <- function(object, split, ..., metrics = NULL, control = control_last_fit()) {
+last_fit.workflow <- function(object, split, ..., metrics = NULL,
+                              control = control_last_fit(),
+                              add_validation_set = FALSE) {
   empty_ellipses(...)
 
   control <- parsnip::condense_control(control, control_last_fit())
 
-  last_fit_workflow(object, split, metrics, control)
+  last_fit_workflow(object, split, metrics, control, add_validation_set)
 }
 
-last_fit_workflow <- function(object, split, metrics, control) {
+last_fit_workflow <- function(object, split, metrics, control, add_validation_set) {
   check_no_tuning(object)
+  if (inherits(split, "initial_validation_split")) {
+    split <- prepare_validation_split(split, add_validation_set)
+  }
   splits <- list(split)
   resamples <- rsample::manual_rset(splits, ids = "train/test split")
 
@@ -132,3 +145,32 @@ last_fit_workflow <- function(object, split, metrics, control) {
   .stash_last_result(res)
   res
 }
+
+
+prepare_validation_split <- function(split, add_validation_set){
+  if (add_validation_set) {
+    # equivalent to (unexported) rsample:::rsplit() without checks
+    split <- structure(
+      list(
+        data = split$data,
+        in_id = c(split$train_id, split$val_id),
+        out_id = NA
+      ),
+      class = "rsplit"
+    )
+  } else {
+    id_train_test <- seq_len(nrow(split$data))[-sort(split$val_id)]
+    id_train <- match(split$train_id, id_train_test)
+
+    split <- structure(
+      list(
+        data = split$data[-sort(split$val_id), , drop = FALSE],
+        in_id = id_train,
+        out_id = NA
+      ),
+      class = "rsplit"
+    )
+  }
+
+  split
+}
diff --git a/R/logging.R b/R/logging.R
index f8f0f2cbd..653f70fbc 100644
--- a/R/logging.R
+++ b/R/logging.R
@@ -256,12 +256,12 @@ siren <- function(x, type = "info") {
 }
 
 
-tune_log <- function(control, split = NULL, task, type = "success") {
-  if (!control$verbose) {
+tune_log <- function(control, split = NULL, task, type = "success", catalog = TRUE) {
+  if (!any(control$verbose, control$verbose_iter)) {
     return(invisible(NULL))
   }
 
-  if (uses_catalog()) {
+  if (uses_catalog() & catalog) {
     log_catalog(task, type)
     return(NULL)
   }
@@ -291,6 +291,7 @@ log_problems <- function(notes, control, split, loc, res, bad_only = FALSE) {
   # Always log warnings and errors
   control2 <- control
   control2$verbose <- TRUE
+  control2$verbose_iter <- TRUE
 
   should_catalog <- uses_catalog()
 
@@ -361,8 +362,8 @@ format_msg <- function(loc, msg) {
 
 #' @export
 #' @rdname tune-internal-functions
-.catch_and_log <- function(.expr, ..., bad_only = FALSE, notes) {
-  tune_log(..., type = "info")
+.catch_and_log <- function(.expr, ..., bad_only = FALSE, notes, catalog = TRUE) {
+  tune_log(..., type = "info", catalog = catalog)
   tmp <- catcher(.expr)
   new_notes <- log_problems(notes, ..., tmp, bad_only = bad_only)
   assign("out_notes", new_notes, envir = parent.frame())
@@ -410,7 +411,7 @@ format_msg <- function(loc, msg) {
 }
 
 log_best <- function(control, iter, info, digits = 4) {
-  if (!control$verbose) {
+  if (!isTRUE(control$verbose_iter)) {
     return(invisible(NULL))
   }
 
@@ -428,16 +429,22 @@ log_best <- function(control, iter, info, digits = 4) {
       info$best_iter,
       ")"
     )
-  tune_log(control, split = NULL, task = msg, type = "info")
+  tune_log(control, split = NULL, task = msg, type = "info", catalog = FALSE)
 }
 
 check_and_log_flow <- function(control, results) {
+  if (!isTRUE(control$verbose_iter)) {
+    return(invisible(NULL))
+  }
+
   if (all(is.na(results$.mean))) {
     if (nrow(results) < 2) {
-      tune_log(control, split = NULL, task = "Halting search", type = "danger")
+      tune_log(control, split = NULL, task = "Halting search",
+               type = "danger", catalog = FALSE)
       eval.parent(parse(text = "break"))
     } else {
-      tune_log(control, split = NULL, task = "Skipping to next iteration", type = "danger")
+      tune_log(control, split = NULL, task = "Skipping to next iteration",
+               type = "danger", catalog = FALSE)
       eval.parent(parse(text = "next"))
     }
   }
@@ -445,7 +452,7 @@ check_and_log_flow <- function(control, results) {
 }
 
 log_progress <- function(control, x, maximize = TRUE, objective = NULL, digits = 4) {
-  if (!control$verbose) {
+  if (!isTRUE(control$verbose_iter)) {
     return(invisible(NULL))
   }
 
@@ -479,7 +486,7 @@ log_progress <- function(control, x, maximize = TRUE, objective = NULL, digits =
 }
 
 param_msg <- function(control, candidate) {
-  if (!control$verbose) {
+  if (!isTRUE(control$verbose_iter)) {
     return(invisible(NULL))
   }
   candidate <- candidate[, !(names(candidate) %in% c(".mean", ".sd", "objective"))]
@@ -495,7 +502,7 @@ param_msg <- function(control, candidate) {
 
 
 acq_summarizer <- function(control, iter, objective = NULL, digits = 4) {
-  if (!control$verbose) {
+  if (!isTRUE(control$verbose_iter)) {
     return(invisible(NULL))
   }
   if (inherits(objective, "conf_bound") && is.function(objective$kappa)) {
@@ -509,7 +516,7 @@ acq_summarizer <- function(control, iter, objective = NULL, digits = 4) {
     }
   }
   if (!is.null(val)) {
-    tune_log(control, split = NULL, task = val, type = "info")
+    tune_log(control, split = NULL, task = val, type = "info", catalog = FALSE)
   }
   invisible(NULL)
 }
diff --git a/R/tune_bayes.R b/R/tune_bayes.R
index ae47a4f29..399d989a7 100644
--- a/R/tune_bayes.R
+++ b/R/tune_bayes.R
@@ -342,7 +342,8 @@ tune_bayes_workflow <-
           control,
           NULL,
           "Gaussian process model",
-          notes = .notes
+          notes = .notes,
+          catalog = FALSE
         )
 
       gp_mod <- check_gp_failure(gp_mod, prev_gp_mod)
@@ -563,13 +564,14 @@ pred_gp <- function(object, pset, size = 5000, current = NULL, control) {
     control,
     split = NULL,
     task = paste("Generating", nrow(pred_grid), "candidates"),
-    type = "info"
+    type = "info",
+    catalog = FALSE
   )
 
   x <- encode_set(pred_grid, pset, as_matrix = TRUE)
   gp_pred <- predict(object, x)
 
-  tune_log(control, split = NULL, task = "Predicted candidates", type = "info")
+  tune_log(control, split = NULL, task = "Predicted candidates", type = "info", catalog = FALSE)
 
   pred_grid %>%
     dplyr::mutate(.mean = gp_pred$Y_hat, .sd = sqrt(gp_pred$MSE))
diff --git a/man/fit_best.Rd b/man/fit_best.Rd
index 794c969bf..38a038426 100644
--- a/man/fit_best.Rd
+++ b/man/fit_best.Rd
@@ -10,7 +10,14 @@ fit_best(x, ...)
 
 \method{fit_best}{default}(x, ...)
 
-\method{fit_best}{tune_results}(x, metric = NULL, parameters = NULL, verbose = FALSE, ...)
+\method{fit_best}{tune_results}(
+  x,
+  metric = NULL,
+  parameters = NULL,
+  verbose = FALSE,
+  add_validation_set = NULL,
+  ...
+)
 }
 \arguments{
 \item{x}{The results of class \code{tune_results} (coming from functions such as
@@ -29,6 +36,13 @@ If \code{NULL}, this argument will be set to
 \code{\link[=select_best.tune_results]{select_best(metric)}}.}
 
 \item{verbose}{A logical for printing logging.}
+
+\item{add_validation_set}{When the resamples embedded in \code{x} are a split into
+training set and validation set, should the validation set be included in the
+data set used to train the model? If not, only the training set is used. If
+\code{NULL}, the validation set is not used for resamples originating from
+\code{\link[rsample:validation_set]{rsample::validation_set()}} while it is used for resamples originating
+from \code{\link[rsample:validation_split]{rsample::validation_split()}}.}
 }
 \value{
 A fitted workflow.
@@ -46,10 +60,6 @@ This function is a shortcut for the manual steps of:
   wflow_fit <- fit(wflow, data_set)
 }
 
-The data used for the fit are taken from the \code{splits} column. If the split
-column was from a validation split, the combined training and validation sets
-are used.
-
 In comparison to \code{\link[=last_fit]{last_fit()}}, that function requires a finalized model, fits
 the model on the training set defined by \code{\link[rsample:initial_split]{rsample::initial_split()}}, and
 computes metrics from the test set.
diff --git a/man/last_fit.Rd b/man/last_fit.Rd
index 2e0bed05d..aa8c1cb7e 100644
--- a/man/last_fit.Rd
+++ b/man/last_fit.Rd
@@ -14,10 +14,18 @@ last_fit(object, ...)
   split,
   ...,
   metrics = NULL,
-  control = control_last_fit()
+  control = control_last_fit(),
+  add_validation_set = FALSE
 )
 
-\method{last_fit}{workflow}(object, split, ..., metrics = NULL, control = control_last_fit())
+\method{last_fit}{workflow}(
+  object,
+  split,
+  ...,
+  metrics = NULL,
+  control = control_last_fit(),
+  add_validation_set = FALSE
+)
 }
 \arguments{
 \item{object}{A \code{parsnip} model specification or a \code{\link[workflows:workflow]{workflows::workflow()}}.
@@ -28,13 +36,19 @@ No tuning parameters are allowed.}
 \item{preprocessor}{A traditional model formula or a recipe created using
 \code{\link[recipes:recipe]{recipes::recipe()}}.}
 
-\item{split}{An \code{rsplit} object created from \code{\link[rsample:initial_split]{rsample::initial_split()}}.}
+\item{split}{An \code{rsplit} object created from \code{\link[rsample:initial_split]{rsample::initial_split()}} or
+\code{\link[rsample:initial_validation_split]{rsample::initial_validation_split()}}.}
 
 \item{metrics}{A \code{\link[yardstick:metric_set]{yardstick::metric_set()}}, or \code{NULL} to compute a standard
 set of metrics.}
 
 \item{control}{A \code{\link[=control_last_fit]{control_last_fit()}} object used to fine tune the last fit
 process.}
+
+\item{add_validation_set}{For 3-way splits into training, validation, and test
+set via \code{\link[rsample:initial_validation_split]{rsample::initial_validation_split()}}, should the validation set be
+included in the data set used to train the model. If not, only the training
+set is used.}
 }
 \value{
 A single row tibble that emulates the structure of \code{fit_resamples()}.
diff --git a/man/tune-internal-functions.Rd b/man/tune-internal-functions.Rd
index 8724d163e..e8d09365d 100644
--- a/man/tune-internal-functions.Rd
+++ b/man/tune-internal-functions.Rd
@@ -29,7 +29,7 @@ finalize_workflow_preprocessor(workflow, grid_preprocessor)
 
 initialize_catalog(control, env = rlang::caller_env())
 
-.catch_and_log(.expr, ..., bad_only = FALSE, notes)
+.catch_and_log(.expr, ..., bad_only = FALSE, notes, catalog = TRUE)
 
 .catch_and_log_fit(.expr, ..., notes)
 }
@@ -63,6 +63,11 @@ included as an argument to allow for pre-computing.}
 \item{bad_only}{A logical for whether warnings and errors should be caught.}
 
 \item{notes}{Character data to add to the logging.}
+
+\item{catalog}{A logical passed to \code{tune_log()} giving whether the message
+is compatible with the issue cataloger. Defaults to \code{TRUE}. Updates that are
+always unique and do not represent a tuning "issue" can bypass the cataloger
+by setting \code{catalog = FALSE}.}
 }
 \description{
 These are not to be meant to be invoked directly by users.
diff --git a/tests/testthat/_snaps/bayes.md b/tests/testthat/_snaps/bayes.md
index 2745f7e17..c9ce46329 100644
--- a/tests/testthat/_snaps/bayes.md
+++ b/tests/testthat/_snaps/bayes.md
@@ -8,10 +8,6 @@
       >  Generating a set of 2 initial parameter results
       v Initialization complete
       
-      
-      -- Iteration 1 -----------------------------------------------------------------
-      
-      i Current best:		rmse=2.418 (@iter 0)
       i Gaussian process model
       ! The Gaussian process model is being fit using 1 features but only has 2
         data points to do so. This may cause errors or a poor model fit.
@@ -19,7 +15,6 @@
       v Gaussian process model
       i Generating 3 candidates
       i Predicted candidates
-      i num_comp=2
       i Estimating performance
       i Fold01: preprocessor 1/1
       v Fold01: preprocessor 1/1
@@ -82,17 +77,11 @@
       i Fold10: preprocessor 1/1, model 1/1 (extracts)
       i Fold10: preprocessor 1/1, model 1/1 (predictions)
       v Estimating performance
-      (x) Newest results:	rmse=2.666 (+/-0.281)
-      
-      -- Iteration 2 -----------------------------------------------------------------
-      
-      i Current best:		rmse=2.418 (@iter 0)
       i Gaussian process model
       ! Gaussian process model: X should be in range (0, 1)
       v Gaussian process model
       i Generating 2 candidates
       i Predicted candidates
-      i num_comp=5
       i Estimating performance
       i Fold01: preprocessor 1/1
       v Fold01: preprocessor 1/1
@@ -155,7 +144,6 @@
       i Fold10: preprocessor 1/1, model 1/1 (extracts)
       i Fold10: preprocessor 1/1, model 1/1 (predictions)
       v Estimating performance
-      (x) Newest results:	rmse=2.453 (+/-0.381)
     Output
       # Tuning results
       # 10-fold cross-validation 
@@ -181,10 +169,34 @@
         control = control_bayes(verbose_iter = TRUE))
     Message
       Optimizing rmse using the expected improvement
+      
+      -- Iteration 1 -----------------------------------------------------------------
+      
+      i Current best:		rmse=2.418 (@iter 0)
+      i Gaussian process model
       ! The Gaussian process model is being fit using 1 features but only has 2
         data points to do so. This may cause errors or a poor model fit.
       ! Gaussian process model: X should be in range (0, 1)
+      v Gaussian process model
+      i Generating 3 candidates
+      i Predicted candidates
+      i num_comp=4
+      i Estimating performance
+      v Estimating performance
+      (x) Newest results:	rmse=2.461 (+/-0.37)
+      
+      -- Iteration 2 -----------------------------------------------------------------
+      
+      i Current best:		rmse=2.418 (@iter 0)
+      i Gaussian process model
       ! Gaussian process model: X should be in range (0, 1)
+      v Gaussian process model
+      i Generating 2 candidates
+      i Predicted candidates
+      i num_comp=5
+      i Estimating performance
+      v Estimating performance
+      (x) Newest results:	rmse=2.453 (+/-0.381)
     Output
       # Tuning results
       # 10-fold cross-validation 
@@ -511,68 +523,28 @@
         Bldg_Type + Latitude + Longitude, resamples = folds, initial = 3, metrics = yardstick::metric_set(
         rsq), param_info = parameters(dials::cost_complexity(c(-2, 0))))
     Message
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9998726`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 1 missing value was found and removed before fitting
         the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9995734`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 2 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9995`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 3 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9992439`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 4 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9996399`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 5 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9994867`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 6 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9997809`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 7 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9999164`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
 
 ---
 
@@ -583,36 +555,11 @@
       metrics = yardstick::metric_set(rsq), param_info = parameters(dials::cost_complexity(
         c(0.5, 0))))
     Message
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 1.95966`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 2.500534`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 1.263317`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 2.792327`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 1.224671`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! All of the rsq estimates were missing. The Gaussian process model cannot be
         fit to the data.
       ! Gaussian process model: no non-missing arguments to min; returning Inf, no non-missing arguments...
diff --git a/tests/testthat/_snaps/resample.md b/tests/testthat/_snaps/resample.md
index 0fb804e8d..df010e0ff 100644
--- a/tests/testthat/_snaps/resample.md
+++ b/tests/testthat/_snaps/resample.md
@@ -39,10 +39,10 @@
     Message
       x Fold1: preprocessor 1/1, model 1/1:
         Error in `check_outcome()`:
-        ! For a classification model, the outcome should be a factor.
+        ! For a classification model, the outcome should be a `factor`, not a ...
       x Fold2: preprocessor 1/1, model 1/1:
         Error in `check_outcome()`:
-        ! For a classification model, the outcome should be a factor.
+        ! For a classification model, the outcome should be a `factor`, not a ...
     Condition
       Warning:
       All models failed. Run `show_notes(.Last.tune.result)` for more information.
diff --git a/tests/testthat/test-compat-dplyr.R b/tests/testthat/test-compat-dplyr.R
index 04d6b9230..f180a999e 100644
--- a/tests/testthat/test-compat-dplyr.R
+++ b/tests/testthat/test-compat-dplyr.R
@@ -291,7 +291,9 @@ test_that("left_join() can keep tune_results class if tune_results structure is
 test_that("left_join() can lose tune_results class if rows are added", {
   for (x in helper_tune_results) {
     y <- tibble(id = x$id[[1]], x = 1:2)
-    expect_s3_class_bare_tibble(left_join(x, y, by = "id", multiple = "all"))
+    expect_s3_class_bare_tibble(
+      left_join(x, y, by = "id", relationship = "many-to-many")
+    )
   }
 })
 
@@ -318,7 +320,9 @@ test_that("right_join() can keep tune_results class if tune_results structure is
 test_that("right_join() can lose tune_results class if rows are added", {
   for (x in helper_tune_results) {
     y <- tibble(id = x$id[[1]], x = 1:2)
-    expect_s3_class_bare_tibble(right_join(x, y, by = "id", multiple = "all"))
+    expect_s3_class_bare_tibble(
+      right_join(x, y, by = "id", relationship = "many-to-many")
+    )
   }
 })
 
diff --git a/tests/testthat/test-extract-helpers.R b/tests/testthat/test-extract-helpers.R
index a86f4fc34..64dcb6bb4 100644
--- a/tests/testthat/test-extract-helpers.R
+++ b/tests/testthat/test-extract-helpers.R
@@ -24,7 +24,7 @@ test_that("extract methods for resample_results objects", {
       recipes::step_normalize(recipes::all_numeric_predictors()))
   lm_rec_res <- fit_resamples(
     lm_rec_wflow,
-    resamples = rsample::vfold_cv(mtcars, V = 2),
+    resamples = rsample::vfold_cv(mtcars, v = 2),
     control = control_resamples(save_workflow = TRUE)
   )
 
diff --git a/tests/testthat/test-fit_best.R b/tests/testthat/test-fit_best.R
index 5e8995e0f..f598f6550 100644
--- a/tests/testthat/test-fit_best.R
+++ b/tests/testthat/test-fit_best.R
@@ -63,3 +63,68 @@ test_that("fit_best", {
     fit_best(ames_iter_search)
   )
 })
+
+test_that("fit_best() works with validation split: 3-way split", {
+  skip_if_not_installed("kknn")
+  skip_if_not_installed("modeldata")
+  data(ames, package = "modeldata", envir = rlang::current_env())
+
+  set.seed(23598723)
+  initial_val_split <- rsample::initial_validation_split(ames)
+  val_set <- validation_set(initial_val_split)
+
+  f <- Sale_Price ~ Gr_Liv_Area + Year_Built
+  knn_mod <- nearest_neighbor(neighbors = tune()) %>% set_mode("regression")
+  wflow <- workflow(f, knn_mod)
+
+  tune_res <- tune_grid(
+    wflow,
+    grid = tibble(neighbors = c(1, 5)),
+    resamples = val_set,
+    control = control_grid(save_workflow = TRUE)
+  ) %>% suppressWarnings()
+  set.seed(3)
+  fit_on_train <- fit_best(tune_res)
+  pred <- predict(fit_on_train, testing(initial_val_split))
+
+  set.seed(3)
+  exp_fit_on_train <- nearest_neighbor(neighbors = 5) %>%
+    set_mode("regression") %>%
+    fit(f, training(initial_val_split))
+  exp_pred <- predict(exp_fit_on_train, testing(initial_val_split))
+
+  expect_equal(pred, exp_pred)
+})
+
+test_that("fit_best() works with validation split: 2x 2-way splits", {
+  skip_if_not_installed("kknn")
+  skip_if_not_installed("modeldata")
+  data(ames, package = "modeldata", envir = rlang::current_env())
+
+  set.seed(23598723)
+  split <- rsample::initial_split(ames)
+  train_and_val <- training(split)
+  val_set <- rsample::validation_split(train_and_val)
+
+  f <- Sale_Price ~ Gr_Liv_Area + Year_Built
+  knn_mod <- nearest_neighbor(neighbors = tune()) %>% set_mode("regression")
+  wflow <- workflow(f, knn_mod)
+
+  tune_res <- tune_grid(
+    wflow,
+    grid = tibble(neighbors = c(1, 5)),
+    resamples = val_set,
+    control = control_grid(save_workflow = TRUE)
+  )
+  set.seed(3)
+  fit_on_train_and_val <- fit_best(tune_res)
+  pred <- predict(fit_on_train_and_val, testing(split))
+
+  set.seed(3)
+  exp_fit_on_train_and_val <- nearest_neighbor(neighbors = 5) %>%
+    set_mode("regression") %>%
+    fit(f, train_and_val)
+  exp_pred <- predict(exp_fit_on_train_and_val, testing(split))
+
+  expect_equal(pred, exp_pred)
+})
diff --git a/tests/testthat/test-last-fit.R b/tests/testthat/test-last-fit.R
index a974799ce..445a8d27f 100644
--- a/tests/testthat/test-last-fit.R
+++ b/tests/testthat/test-last-fit.R
@@ -146,3 +146,68 @@ test_that("`last_fit()` when objects need tuning", {
   expect_snapshot_error(last_fit(wflow_2, split))
   expect_snapshot_error(last_fit(wflow_3, split))
 })
+
+test_that("last_fit() excludes validation set for initial_validation_split objects", {
+  skip_if_not_installed("modeldata")
+  data(ames, package = "modeldata", envir = rlang::current_env())
+
+  set.seed(23598723)
+  split <- rsample::initial_validation_split(ames)
+
+  f <- Sale_Price ~ Gr_Liv_Area + Year_Built
+  lm_fit <- lm(f, data = rsample::training(split))
+  test_pred <- predict(lm_fit, rsample::testing(split))
+  rmse_test <- yardstick::rsq_vec(rsample::testing(split) %>% pull(Sale_Price), test_pred)
+
+  res <- parsnip::linear_reg() %>%
+    parsnip::set_engine("lm") %>%
+    last_fit(f, split)
+
+  expect_equal(res, .Last.tune.result)
+
+  expect_equal(
+    coef(extract_fit_engine(res$.workflow[[1]])),
+    coef(lm_fit),
+    ignore_attr = TRUE
+  )
+  expect_equal(res$.metrics[[1]]$.estimate[[2]], rmse_test)
+  expect_equal(res$.predictions[[1]]$.pred, unname(test_pred))
+  expect_true(res$.workflow[[1]]$trained)
+  expect_equal(
+    nrow(predict(res$.workflow[[1]], rsample::testing(split))),
+    nrow(rsample::testing(split))
+  )
+})
+
+test_that("last_fit() can include validation set for initial_validation_split objects", {
+  skip_if_not_installed("modeldata")
+  data(ames, package = "modeldata", envir = rlang::current_env())
+
+  set.seed(23598723)
+  split <- rsample::initial_validation_split(ames)
+
+  f <- Sale_Price ~ Gr_Liv_Area + Year_Built
+  train_val <- rbind(rsample::training(split), rsample::validation(split))
+  lm_fit <- lm(f, data = train_val)
+  test_pred <- predict(lm_fit, rsample::testing(split))
+  rmse_test <- yardstick::rsq_vec(rsample::testing(split) %>% pull(Sale_Price), test_pred)
+
+  res <- parsnip::linear_reg() %>%
+    parsnip::set_engine("lm") %>%
+    last_fit(f, split, add_validation_set = TRUE)
+
+  expect_equal(res, .Last.tune.result)
+
+  expect_equal(
+    coef(extract_fit_engine(res$.workflow[[1]])),
+    coef(lm_fit),
+    ignore_attr = TRUE
+  )
+  expect_equal(res$.metrics[[1]]$.estimate[[2]], rmse_test)
+  expect_equal(res$.predictions[[1]]$.pred, unname(test_pred))
+  expect_true(res$.workflow[[1]]$trained)
+  expect_equal(
+    nrow(predict(res$.workflow[[1]], rsample::testing(split))),
+    nrow(rsample::testing(split))
+  )
+})
diff --git a/tests/testthat/test-logging.R b/tests/testthat/test-logging.R
index 6d202548f..1ac4bbcbf 100644
--- a/tests/testthat/test-logging.R
+++ b/tests/testthat/test-logging.R
@@ -82,8 +82,8 @@ test_that("catch and log issues", {
 })
 
 test_that("logging iterations", {
-  ctrl_t <- control_grid(verbose = TRUE)
-  ctrl_f <- control_grid(verbose = FALSE)
+  ctrl_t <- control_bayes(verbose_iter = TRUE)
+  ctrl_f <- control_bayes(verbose_iter = FALSE)
   sc_1 <- list(
     best_val = 7,
     best_iter = 2,
@@ -99,7 +99,7 @@ test_that("logging iterations", {
 })
 
 test_that("logging search info", {
-  ctrl_t <- control_grid(verbose = TRUE)
+  ctrl_t <- control_bayes(verbose_iter = TRUE)
   tb_1 <- tibble::tibble(.mean = 1:3)
 
   expect_silent(tune:::check_and_log_flow(ctrl_t, tb_1))
@@ -114,8 +114,8 @@ test_that("logging search info", {
 })
 
 test_that("current results", {
-  ctrl_t <- control_grid(verbose = TRUE)
-  ctrl_f <- control_grid(verbose = FALSE)
+  ctrl_t <- control_bayes(verbose_iter = TRUE)
+  ctrl_f <- control_bayes(verbose_iter = FALSE)
   tb_2 <-
     tibble::tibble(
       .metric = rep(letters[1:2], each = 4),
@@ -141,8 +141,8 @@ test_that("current results", {
 
 
 test_that("show parameters", {
-  ctrl_t <- control_grid(verbose = TRUE)
-  ctrl_f <- control_grid(verbose = FALSE)
+  ctrl_t <- control_bayes(verbose_iter = TRUE)
+  ctrl_f <- control_bayes(verbose_iter = FALSE)
 
   expect_snapshot(tune:::param_msg(ctrl_t, iris[1, 4:5]))
   expect_silent(tune:::param_msg(ctrl_f, iris[1, 4:5]))
@@ -150,8 +150,8 @@ test_that("show parameters", {
 
 
 test_that("acquisition functions", {
-  ctrl_t <- control_grid(verbose = TRUE)
-  ctrl_f <- control_grid(verbose = FALSE)
+  ctrl_t <- control_bayes(verbose_iter = TRUE)
+  ctrl_f <- control_bayes(verbose_iter = FALSE)
 
   expect_silent(tune:::acq_summarizer(ctrl_t, 1))
   expect_silent(tune:::acq_summarizer(ctrl_t, 1, conf_bound()))
diff --git a/tests/testthat/test-resample.R b/tests/testthat/test-resample.R
index 54f2820b6..1d4c8827f 100644
--- a/tests/testthat/test-resample.R
+++ b/tests/testthat/test-resample.R
@@ -221,13 +221,12 @@ test_that("classification models generate correct error message", {
   expect_length(notes, 2L)
 
   # Known failure in the recipe
-  expect_true(all(grepl("outcome should be a factor", note)))
+  expect_true(all(grepl("outcome should be a `factor`", note)))
 
   expect_equal(extract, list(NULL, NULL))
   expect_equal(predictions, list(NULL, NULL))
 })
 
-
 # tune_grid() fallback ---------------------------------------------------------
 
 test_that("`tune_grid()` falls back to `fit_resamples()` - formula", {