diff --git a/R/schedule.R b/R/schedule.R
index 28b85843..24ff5330 100644
--- a/R/schedule.R
+++ b/R/schedule.R
@@ -83,11 +83,22 @@ get_tune_schedule <- function(wflow, param, grid) {
 	if (has_submodels) {
 		sched <- grid %>%
 			dplyr::group_nest(!!!symbs$fits, .key = "predict_stage")
-		# Note: multi_predict() should only be triggered for a submodel parameter if
+		# Note 1: multi_predict() should only be triggered for a submodel parameter if
 		# there are multiple rows in the `predict_stage` list column. i.e. the submodel
 		# column will always be there but we only multipredict when there are 2+
 		# values to predict.
-		first_loop_info <- min_grid(model_spec, grid)
+
+		# Note 2: The purpose of min_grid() is to determine the minimum grid for
+		# preprocessing and model parameters to fit. We compute it here and ignore
+		# any postprocessing tuning parmeters (if any). The postprocessing parameters
+		# will still be in the schedule since we schedule those before the results
+		# that use min_grid() are merged in. See issue #975 for an example and
+		# discussion.
+		first_loop_info <-
+			min_grid(model_spec,
+							 grid %>%
+							 	dplyr::select(-dplyr::any_of(post_id)) %>%
+							 	dplyr::distinct())
 	} else {
 		sched <- grid %>%
 			dplyr::group_nest(!!!symbs$fits, .key = "predict_stage")
diff --git a/tests/testthat/test-schedule.R b/tests/testthat/test-schedule.R
index 3a0262a9..2e519aa4 100644
--- a/tests/testthat/test-schedule.R
+++ b/tests/testthat/test-schedule.R
@@ -645,31 +645,6 @@ test_that("grid processing schedule - recipe + model + tailor, submodels, irregu
 																						prm_used_pre_model_post,
 																						grid_pre_model_post)
 
-	# TODO trees seems to have an extra row:
-
-	# # A tibble: 4 × 3
-	# 	min_n predict_stage    trees
-	# 	<int> <list>           <int>
-	# 1     2 <tibble [1 × 2]>     1
-	# 2    21 <tibble [1 × 2]>     1
-	# 3    40 <tibble [2 × 2]>  1000  #<- shouldn't this row and the one below be combined?
-	# 4    40 <tibble [2 × 2]>     1
-
-	# sched_pre_model_post$model_stage[[1]] %>%
-	# 	select(-trees) %>%
-	# 	unnest(predict_stage) %>%
-	# 	unnest(post_stage) %>%
-	# 	arrange(min_n, trees, lower_limit)
-
-	# tibble::tribble(
-	# 	~min_n, ~trees, ~lower_limit, ~trees0,
-	# 	    2L,     1L,            0,      1L,
-	#      21L,     1L,          0.5,      1L,
-	# 	   40L,     1L,            1,   1000L,
-	# 	   40L,     1L,            1,      1L,
-	# 	   40L,  1000L,            0,   1000L,
-	# 	   40L,  1000L,            0,      1L
-	# )
 
 	expect_named(sched_pre_model_post, c("threshold", "disp_df", "model_stage"))
 	expect_equal(
@@ -677,43 +652,61 @@ test_that("grid processing schedule - recipe + model + tailor, submodels, irregu
 		grid_pre %>% arrange(threshold, disp_df)
 	)
 
-	# for (i in seq_along(sched_pre_model_post$model_stage)) {
-	# 	model_i <- sched_pre_model_post$model_stage[[i]]
-	# 	expect_named(model_i, c("min_n", "predict_stage", "trees"))
-	# 	expect_equal(
-	# 		model_i %>% select(min_n, trees) %>% arrange(min_n),
-	# 		grid_model$data[[i]]
-	# 	)
-	#
-	# for (j in seq_along(sched_pre_model_post$model_stage[[i]]$predict_stage)) {
-	# 	predict_j <- model_i$predict_stage[[j]]
-	#
-	# 	# We need to figure out the trees that need predicting for the current
-	# 	# set of other parameters.
-	#
-	# 	# Get the settings that have already be resolved:
-	# 	other_ij <-
-	# 		model_i %>%
-	# 		select(-predict_stage, -trees) %>%
-	# 		slice(j) %>%
-	# 		vctrs::vec_cbind(
-	# 			sched_pre_model_post %>%
-	# 				select(threshold, disp_df) %>%
-	# 				slice(i)
-	# 		)
-	# 	# What are the matching values from the grid?
-	# 	trees_ij <-
-	# 		grid_pre_model_post %>%
-	# 		inner_join(other_ij, by = c("min_n", "threshold", "disp_df")) %>%
-	# 		select(trees)
-	#
-	#
-	# 	expect_equal(
-	# 		predict_j %>% select(trees) %>% arrange(trees),
-	# 		trees_ij %>% arrange(trees)
-	# 	)
-	# }
-	# }
+	for (i in seq_along(sched_pre_model_post$model_stage)) {
+		model_i <- sched_pre_model_post$model_stage[[i]]
+
+		# Get the current set of preproc parameters to remove
+		other_i <-
+			sched_pre_model_post[i,] %>%
+			dplyr::select(-model_stage)
+
+		# We expect to evaulate these specific models for this set of preprocessors
+		exp_i <-
+			grid_pre_model_post %>%
+			inner_join(other_i, by = c("threshold", "disp_df")) %>%
+			arrange(trees, min_n, lower_limit) %>%
+			select(trees, min_n, lower_limit)
+
+		# What we will evaluate:
+		subgrid_i <-
+			model_i %>%
+			select(-trees) %>%
+			unnest(predict_stage) %>%
+			unnest(post_stage) %>%
+			arrange(trees, min_n, lower_limit) %>%
+			select(trees, min_n, lower_limit)
+
+		expect_equal(subgrid_i, exp_i)
+
+		# for (j in seq_along(sched_pre_model_post$model_stage[[i]]$predict_stage)) {
+		# 	predict_j <- model_i$predict_stage[[j]]
+		#
+		# 	# We need to figure out the trees that need predicting for the current
+		# 	# set of other parameters.
+		#
+		# 	# Get the settings that have already be resolved:
+		# 	other_ij <-
+		# 		model_i %>%
+		# 		select(-predict_stage, -trees) %>%
+		# 		slice(j) %>%
+		# 		vctrs::vec_cbind(
+		# 			sched_pre_model_post %>%
+		# 				select(threshold, disp_df) %>%
+		# 				slice(i)
+		# 		)
+		# 	# What are the matching values from the grid?
+		# 	trees_ij <-
+		# 		grid_pre_model_post %>%
+		# 		inner_join(other_ij, by = c("min_n", "threshold", "disp_df")) %>%
+		# 		select(trees)
+		#
+		#
+		# 	expect_equal(
+		# 		predict_j %>% select(trees) %>% arrange(trees),
+		# 		trees_ij %>% arrange(trees)
+		# 	)
+		# }
+	}
 
 	expect_s3_class(
 		sched_pre_model_post,