diff --git a/R-package/tests/testthat/helper.R b/R-package/tests/testthat/helper.R
index 9da2f9bd7167..9c928c1f71d1 100644
--- a/R-package/tests/testthat/helper.R
+++ b/R-package/tests/testthat/helper.R
@@ -29,3 +29,20 @@
 .LGB_VERBOSITY <- as.integer(
   Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
+
+# [description]
+#    test that every element of 'x' is in 'y'
+#
+#    testthat::expect_in() is not available in version of {testthat}
+#    built for R 3.6, this is here to support a similar interface on R 3.6
+.expect_in <- function(x, y) {
+  if (exists("expect_in")) {
+    expect_in(x, y)
+  } else {
+    missing_items <- x[!(x %in% y)]
+    if (length(missing_items) != 0L) {
+      error_msg <- paste0("Some expected items not found: ", toString(missing_items))
+      stop(error_msg)
+    }
+  }
+}
diff --git a/R-package/tests/testthat/test_lgb.Booster.R b/R-package/tests/testthat/test_lgb.Booster.R
index 1ff038598db1..5f398f1c081d 100644
--- a/R-package/tests/testthat/test_lgb.Booster.R
+++ b/R-package/tests/testthat/test_lgb.Booster.R
@@ -799,37 +799,166 @@ test_that("all parameters are stored correctly with save_model_to_string()", {
         data = matrix(rnorm(500L), nrow = 100L)
         , label = rnorm(100L)
     )
-    nrounds <- 4L
     bst <- lgb.train(
         params = list(
-            objective = "regression"
-            , metric = "l2"
+            objective = "mape"
+            , metric = c("l2", "mae")
             , num_threads = .LGB_MAX_THREADS
+            , seed = 708L
+            , data_sample_strategy = "bagging"
+            , sub_row = 0.8234
         )
         , data = dtrain
-        , nrounds = nrounds
+        , nrounds = 3L
         , verbose = .LGB_VERBOSITY
     )
 
-    model_str <- bst$save_model_to_string()
-    params_in_file <- .params_from_model_string(model_str = model_str)
+    # entries whose values should reflect params passed to lgb.train()
+    non_default_param_entries <- c(
+        "[objective: mape]"
+        # 'l1' was passed in with alias 'mae'
+        , "[metric: l2,l1]"
+        , "[data_sample_strategy: bagging]"
+        , "[seed: 708]"
+        # this was passed in with alias 'sub_row'
+        , "[bagging_fraction: 0.8234]"
+        , "[num_iterations: 3]"
+    )
+
+    # entries with default values of params
+    default_param_entries <- c(
+        "[boosting: gbdt]"
+        , "[tree_learner: serial]"
+        , "[device_type: cpu]"
+        , "[data: ]"
+        , "[valid: ]"
+        , "[learning_rate: 0.1]"
+        , "[num_leaves: 31]"
+        , sprintf("[num_threads: %i]", .LGB_MAX_THREADS)
+        , "[deterministic: 0]"
+        , "[histogram_pool_size: -1]"
+        , "[max_depth: -1]"
+        , "[min_data_in_leaf: 20]"
+        , "[min_sum_hessian_in_leaf: 0.001]"
+        , "[pos_bagging_fraction: 1]"
+        , "[neg_bagging_fraction: 1]"
+        , "[bagging_freq: 0]"
+        , "[bagging_seed: 15415]"
+        , "[feature_fraction: 1]"
+        , "[feature_fraction_bynode: 1]"
+        , "[feature_fraction_seed: 32671]"
+        , "[extra_trees: 0]"
+        , "[extra_seed: 6642]"
+        , "[early_stopping_round: 0]"
+        , "[first_metric_only: 0]"
+        , "[max_delta_step: 0]"
+        , "[lambda_l1: 0]"
+        , "[lambda_l2: 0]"
+        , "[linear_lambda: 0]"
+        , "[min_gain_to_split: 0]"
+        , "[drop_rate: 0.1]"
+        , "[max_drop: 50]"
+        , "[skip_drop: 0.5]"
+        , "[xgboost_dart_mode: 0]"
+        , "[uniform_drop: 0]"
+        , "[drop_seed: 20623]"
+        , "[top_rate: 0.2]"
+        , "[other_rate: 0.1]"
+        , "[min_data_per_group: 100]"
+        , "[max_cat_threshold: 32]"
+        , "[cat_l2: 10]"
+        , "[cat_smooth: 10]"
+        , "[max_cat_to_onehot: 4]"
+        , "[top_k: 20]"
+        , "[monotone_constraints: ]"
+        , "[monotone_constraints_method: basic]"
+        , "[monotone_penalty: 0]"
+        , "[feature_contri: ]"
+        , "[forcedsplits_filename: ]"
+        , "[force_col_wise: 0]"
+        , "[force_row_wise: 0]"
+        , "[refit_decay_rate: 0.9]"
+        , "[cegb_tradeoff: 1]"
+        , "[cegb_penalty_split: 0]"
+        , "[cegb_penalty_feature_lazy: ]"
+        , "[cegb_penalty_feature_coupled: ]"
+        , "[path_smooth: 0]"
+        , "[interaction_constraints: ]"
+        , sprintf("[verbosity: %i]", .LGB_VERBOSITY)
+        , "[saved_feature_importance_type: 0]"
+        , "[use_quantized_grad: 0]"
+        , "[num_grad_quant_bins: 4]"
+        , "[quant_train_renew_leaf: 0]"
+        , "[stochastic_rounding: 1]"
+        , "[linear_tree: 0]"
+        , "[max_bin: 255]"
+        , "[max_bin_by_feature: ]"
+        , "[min_data_in_bin: 3]"
+        , "[bin_construct_sample_cnt: 200000]"
+        , "[data_random_seed: 2350]"
+        , "[is_enable_sparse: 1]"
+        , "[enable_bundle: 1]"
+        , "[use_missing: 1]"
+        , "[zero_as_missing: 0]"
+        , "[feature_pre_filter: 1]"
+        , "[pre_partition: 0]"
+        , "[two_round: 0]"
+        , "[header: 0]"
+        , "[label_column: ]"
+        , "[weight_column: ]"
+        , "[group_column: ]"
+        , "[ignore_column: ]"
+        , "[categorical_feature: ]"
+        , "[forcedbins_filename: ]"
+        , "[precise_float_parser: 0]"
+        , "[parser_config_file: ]"
+        , "[objective_seed: 4309]"
+        , "[num_class: 1]"
+        , "[is_unbalance: 0]"
+        , "[scale_pos_weight: 1]"
+        , "[sigmoid: 1]"
+        , "[boost_from_average: 1]"
+        , "[reg_sqrt: 0]"
+        , "[alpha: 0.9]"
+        , "[fair_c: 1]"
+        , "[poisson_max_delta_step: 0.7]"
+        , "[tweedie_variance_power: 1.5]"
+        , "[lambdarank_truncation_level: 30]"
+        , "[lambdarank_norm: 1]"
+        , "[label_gain: ]"
+        , "[lambdarank_position_bias_regularization: 0]"
+        , "[eval_at: ]"
+        , "[multi_error_top_k: 1]"
+        , "[auc_mu_weights: ]"
+        , "[num_machines: 1]"
+        , "[local_listen_port: 12400]"
+        , "[time_out: 120]"
+        , "[machine_list_filename: ]"
+        , "[machines: ]"
+        , "[gpu_platform_id: -1]"
+        , "[gpu_device_id: -1]"
+        , "[gpu_use_dp: 0]"
+        , "[num_gpu: 1]"
+    )
+    all_param_entries <- c(non_default_param_entries, default_param_entries)
 
     # parameters should match what was passed from the R package
-    expect_equal(sum(startsWith(params_in_file, "[metric:")), 1L)
-    expect_equal(sum(params_in_file == "[metric: l2]"), 1L)
-
-    expect_equal(sum(startsWith(params_in_file, "[num_iterations:")), 1L)
-    expect_equal(sum(params_in_file == "[num_iterations: 4]"), 1L)
-
-    expect_equal(sum(startsWith(params_in_file, "[objective:")), 1L)
-    expect_equal(sum(params_in_file == "[objective: regression]"), 1L)
-
-    expect_equal(sum(startsWith(params_in_file, "[verbosity:")), 1L)
-    expect_equal(sum(params_in_file == sprintf("[verbosity: %i]", .LGB_VERBOSITY)), 1L)
+    model_str <- bst$save_model_to_string()
+    params_in_file <- .params_from_model_string(model_str = model_str)
+    .expect_in(all_param_entries, params_in_file)
 
     # early stopping should be off by default
     expect_equal(sum(startsWith(params_in_file, "[early_stopping_round:")), 1L)
     expect_equal(sum(params_in_file == "[early_stopping_round: 0]"), 1L)
+
+    # since save_model_to_string() is used when serializing with saveRDS(), check that parameters all
+    # roundtrip saveRDS()/loadRDS() successfully
+    rds_file <- tempfile()
+    saveRDS(bst, rds_file)
+    bst_rds <- readRDS(rds_file)
+    model_str <- bst_rds$save_model_to_string()
+    params_in_file <- .params_from_model_string(model_str = model_str)
+    .expect_in(all_param_entries, params_in_file)
 })
 
 test_that("early_stopping, num_iterations are stored correctly in model string even with aliases", {
diff --git a/helpers/parameter_generator.py b/helpers/parameter_generator.py
index 407f2c73e1e3..a554ee60b6c9 100644
--- a/helpers/parameter_generator.py
+++ b/helpers/parameter_generator.py
@@ -330,7 +330,7 @@ def gen_parameter_code(
     str_to_write += '  std::string tmp_str = "";\n'
     for x in infos:
         for y in x:
-            if "[doc-only]" in y:
+            if "[no-automatically-extract]" in y:
                 continue
             param_type = y["inner_type"][0]
             name = y["name"][0]
@@ -345,7 +345,7 @@ def gen_parameter_code(
     str_to_write += "  std::stringstream str_buf;\n"
     for x in infos:
         for y in x:
-            if "[doc-only]" in y or "[no-save]" in y:
+            if "[no-save]" in y:
                 continue
             param_type = y["inner_type"][0]
             name = y["name"][0]
diff --git a/include/LightGBM/config.h b/include/LightGBM/config.h
index cccf68f360a1..8c03f4d457dd 100644
--- a/include/LightGBM/config.h
+++ b/include/LightGBM/config.h
@@ -5,8 +5,13 @@
  * \note
  * - desc and descl2 fields must be written in reStructuredText format;
  * - nested sections can be placed only at the bottom of parent's section;
- * - [doc-only] tag indicates that only documentation for this param should be generated and all other actions are performed manually;
- * - [no-save] tag indicates that this param should not be saved into a model text representation.
+ * - [no-automatically-extract]
+ *       - do not automatically extract this parameter into a Config property with the same name in Config::GetMembersFromString(). Use if:
+ *           - specialized extraction logic for this param exists in Config::GetMembersFromString()
+ * - [no-save]
+ *       - this param should not be saved into a model text representation via Config::SaveMembersToString(). Use if:
+ *           - param is only used by the CLI (especially the "predict" and "convert_model" tasks)
+ *           - param is related to LightGBM writing files (e.g. "output_model", "save_binary")
  */
 #ifndef LIGHTGBM_CONFIG_H_
 #define LIGHTGBM_CONFIG_H_
@@ -101,15 +106,15 @@ struct Config {
   #pragma region Core Parameters
   #endif  // __NVCC__
 
+  // [no-automatically-extract]
   // [no-save]
-  // [doc-only]
   // alias = config_file
   // desc = path of config file
   // desc = **Note**: can be used only in CLI version
   std::string config = "";
 
+  // [no-automatically-extract]
   // [no-save]
-  // [doc-only]
   // type = enum
   // default = train
   // options = train, predict, convert_model, refit
@@ -122,7 +127,8 @@ struct Config {
   // desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent functions
   TaskType task = TaskType::kTrain;
 
-  // [doc-only]
+  // [no-automatically-extract]
+  // [no-save]
   // type = enum
   // options = regression, regression_l1, huber, fair, poisson, quantile, mape, gamma, tweedie, binary, multiclass, multiclassova, cross_entropy, cross_entropy_lambda, lambdarank, rank_xendcg
   // alias = objective_type, app, application, loss
@@ -154,7 +160,8 @@ struct Config {
   // descl2 = label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)
   std::string objective = "regression";
 
-  // [doc-only]
+  // [no-automatically-extract]
+  // [no-save]
   // type = enum
   // alias = boosting_type, boost
   // options = gbdt, rf, dart
@@ -164,7 +171,7 @@ struct Config {
   // descl2 = **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations
   std::string boosting = "gbdt";
 
-  // [doc-only]
+  // [no-automatically-extract]
   // type = enum
   // options = bagging, goss
   // desc = ``bagging``, Randomly Bagging Sampling
@@ -204,7 +211,8 @@ struct Config {
   // desc = max number of leaves in one tree
   int num_leaves = kDefaultNumLeaves;
 
-  // [doc-only]
+  // [no-automatically-extract]
+  // [no-save]
   // type = enum
   // options = serial, feature, data, voting
   // alias = tree, tree_type, tree_learner_type
@@ -226,7 +234,8 @@ struct Config {
   // desc = **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors
   int num_threads = 0;
 
-  // [doc-only]
+  // [no-automatically-extract]
+  // [no-save]
   // type = enum
   // options = cpu, gpu, cuda
   // alias = device
@@ -239,7 +248,7 @@ struct Config {
   // desc = **Note**: refer to `Installation Guide <./Installation-Guide.rst#build-gpu-version>`__ to build LightGBM with GPU support
   std::string device_type = "cpu";
 
-  // [doc-only]
+  // [no-automatically-extract]
   // alias = random_seed, random_state
   // default = None
   // desc = this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``, etc.
@@ -597,7 +606,6 @@ struct Config {
   // desc = **Note**: can be used only in CLI version
   int snapshot_freq = -1;
 
-  // [no-save]
   // desc = whether to use gradient quantization when training
   // desc = enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins``
   // desc = with quantized training, most arithmetics in the training process will be integer operations
@@ -606,21 +614,18 @@ struct Config {
   // desc = *New in version 4.0.0*
   bool use_quantized_grad = false;
 
-  // [no-save]
   // desc = number of bins to quantization gradients and hessians
   // desc = with more bins, the quantized training will be closer to full precision training
   // desc = **Note**: can be used only with ``device_type = cpu``
   // desc = *New in 4.0.0*
   int num_grad_quant_bins = 4;
 
-  // [no-save]
   // desc = whether to renew the leaf values with original gradients when quantized training
   // desc = renewing is very helpful for good quantized training accuracy for ranking objectives
   // desc = **Note**: can be used only with ``device_type = cpu``
   // desc = *New in 4.0.0*
   bool quant_train_renew_leaf = false;
 
-  // [no-save]
   // desc = whether to use stochastic rounding in gradient quantization
   // desc = *New in 4.0.0*
   bool stochastic_rounding = true;
@@ -980,7 +985,8 @@ struct Config {
   #pragma region Metric Parameters
   #endif  // __NVCC__
 
-  // [doc-only]
+  // [no-automatically-extract]
+  // [no-save]
   // alias = metrics, metric_types
   // default = ""
   // type = multi-enum
diff --git a/src/io/config_auto.cpp b/src/io/config_auto.cpp
index 8182c9b52b93..394614af3f33 100644
--- a/src/io/config_auto.cpp
+++ b/src/io/config_auto.cpp
@@ -664,12 +664,14 @@ void Config::GetMembersFromString(const std::unordered_map<std::string, std::str
 
 std::string Config::SaveMembersToString() const {
   std::stringstream str_buf;
+  str_buf << "[data_sample_strategy: " << data_sample_strategy << "]\n";
   str_buf << "[data: " << data << "]\n";
   str_buf << "[valid: " << Common::Join(valid, ",") << "]\n";
   str_buf << "[num_iterations: " << num_iterations << "]\n";
   str_buf << "[learning_rate: " << learning_rate << "]\n";
   str_buf << "[num_leaves: " << num_leaves << "]\n";
   str_buf << "[num_threads: " << num_threads << "]\n";
+  str_buf << "[seed: " << seed << "]\n";
   str_buf << "[deterministic: " << deterministic << "]\n";
   str_buf << "[force_col_wise: " << force_col_wise << "]\n";
   str_buf << "[force_row_wise: " << force_row_wise << "]\n";
@@ -722,6 +724,10 @@ std::string Config::SaveMembersToString() const {
   str_buf << "[interaction_constraints: " << interaction_constraints << "]\n";
   str_buf << "[verbosity: " << verbosity << "]\n";
   str_buf << "[saved_feature_importance_type: " << saved_feature_importance_type << "]\n";
+  str_buf << "[use_quantized_grad: " << use_quantized_grad << "]\n";
+  str_buf << "[num_grad_quant_bins: " << num_grad_quant_bins << "]\n";
+  str_buf << "[quant_train_renew_leaf: " << quant_train_renew_leaf << "]\n";
+  str_buf << "[stochastic_rounding: " << stochastic_rounding << "]\n";
   str_buf << "[linear_tree: " << linear_tree << "]\n";
   str_buf << "[max_bin: " << max_bin << "]\n";
   str_buf << "[max_bin_by_feature: " << Common::Join(max_bin_by_feature, ",") << "]\n";
diff --git a/tests/python_package_test/test_engine.py b/tests/python_package_test/test_engine.py
index e55df17607b4..b46526bcfaf6 100644
--- a/tests/python_package_test/test_engine.py
+++ b/tests/python_package_test/test_engine.py
@@ -1534,6 +1534,203 @@ def train_and_predict(init_model=None, return_model=False):
         assert ret_origin == pytest.approx(ret)
 
 
+def test_all_expected_params_are_written_out_to_model_text(tmp_path):
+    X, y = make_synthetic_regression()
+    params = {
+        'objective': 'mape',
+        'metric': ['l2', 'mae'],
+        'seed': 708,
+        'data_sample_strategy': 'bagging',
+        'sub_row': 0.8234,
+        'verbose': -1
+    }
+    dtrain = lgb.Dataset(data=X, label=y)
+    gbm = lgb.train(
+        params=params,
+        train_set=dtrain,
+        num_boost_round=3
+    )
+
+    model_txt_from_memory = gbm.model_to_string()
+    model_file = tmp_path / "out.model"
+    gbm.save_model(filename=model_file)
+    with open(model_file, "r") as f:
+        model_txt_from_file = f.read()
+
+    assert model_txt_from_memory == model_txt_from_file
+
+    # entries whose values should reflect params passed to lgb.train()
+    non_default_param_entries = [
+        "[objective: mape]",
+        # 'l1' was passed in with alias 'mae'
+        "[metric: l2,l1]",
+        "[data_sample_strategy: bagging]",
+        "[seed: 708]",
+        # NOTE: this was passed in with alias 'sub_row'
+        "[bagging_fraction: 0.8234]",
+        "[num_iterations: 3]",
+    ]
+
+    # entries with default values of params
+    default_param_entries = [
+        "[boosting: gbdt]",
+        "[tree_learner: serial]",
+        "[data: ]",
+        "[valid: ]",
+        "[learning_rate: 0.1]",
+        "[num_leaves: 31]",
+        "[num_threads: 0]",
+        "[deterministic: 0]",
+        "[histogram_pool_size: -1]",
+        "[max_depth: -1]",
+        "[min_data_in_leaf: 20]",
+        "[min_sum_hessian_in_leaf: 0.001]",
+        "[pos_bagging_fraction: 1]",
+        "[neg_bagging_fraction: 1]",
+        "[bagging_freq: 0]",
+        "[bagging_seed: 15415]",
+        "[feature_fraction: 1]",
+        "[feature_fraction_bynode: 1]",
+        "[feature_fraction_seed: 32671]",
+        "[extra_trees: 0]",
+        "[extra_seed: 6642]",
+        "[early_stopping_round: 0]",
+        "[first_metric_only: 0]",
+        "[max_delta_step: 0]",
+        "[lambda_l1: 0]",
+        "[lambda_l2: 0]",
+        "[linear_lambda: 0]",
+        "[min_gain_to_split: 0]",
+        "[drop_rate: 0.1]",
+        "[max_drop: 50]",
+        "[skip_drop: 0.5]",
+        "[xgboost_dart_mode: 0]",
+        "[uniform_drop: 0]",
+        "[drop_seed: 20623]",
+        "[top_rate: 0.2]",
+        "[other_rate: 0.1]",
+        "[min_data_per_group: 100]",
+        "[max_cat_threshold: 32]",
+        "[cat_l2: 10]",
+        "[cat_smooth: 10]",
+        "[max_cat_to_onehot: 4]",
+        "[top_k: 20]",
+        "[monotone_constraints: ]",
+        "[monotone_constraints_method: basic]",
+        "[monotone_penalty: 0]",
+        "[feature_contri: ]",
+        "[forcedsplits_filename: ]",
+        "[refit_decay_rate: 0.9]",
+        "[cegb_tradeoff: 1]",
+        "[cegb_penalty_split: 0]",
+        "[cegb_penalty_feature_lazy: ]",
+        "[cegb_penalty_feature_coupled: ]",
+        "[path_smooth: 0]",
+        "[interaction_constraints: ]",
+        "[verbosity: -1]",
+        "[saved_feature_importance_type: 0]",
+        "[use_quantized_grad: 0]",
+        "[num_grad_quant_bins: 4]",
+        "[quant_train_renew_leaf: 0]",
+        "[stochastic_rounding: 1]",
+        "[linear_tree: 0]",
+        "[max_bin: 255]",
+        "[max_bin_by_feature: ]",
+        "[min_data_in_bin: 3]",
+        "[bin_construct_sample_cnt: 200000]",
+        "[data_random_seed: 2350]",
+        "[is_enable_sparse: 1]",
+        "[enable_bundle: 1]",
+        "[use_missing: 1]",
+        "[zero_as_missing: 0]",
+        "[feature_pre_filter: 1]",
+        "[pre_partition: 0]",
+        "[two_round: 0]",
+        "[header: 0]",
+        "[label_column: ]",
+        "[weight_column: ]",
+        "[group_column: ]",
+        "[ignore_column: ]",
+        "[categorical_feature: ]",
+        "[forcedbins_filename: ]",
+        "[precise_float_parser: 0]",
+        "[parser_config_file: ]",
+        "[objective_seed: 4309]",
+        "[num_class: 1]",
+        "[is_unbalance: 0]",
+        "[scale_pos_weight: 1]",
+        "[sigmoid: 1]",
+        "[boost_from_average: 1]",
+        "[reg_sqrt: 0]",
+        "[alpha: 0.9]",
+        "[fair_c: 1]",
+        "[poisson_max_delta_step: 0.7]",
+        "[tweedie_variance_power: 1.5]",
+        "[lambdarank_truncation_level: 30]",
+        "[lambdarank_norm: 1]",
+        "[label_gain: ]",
+        "[lambdarank_position_bias_regularization: 0]",
+        "[eval_at: ]",
+        "[multi_error_top_k: 1]",
+        "[auc_mu_weights: ]",
+        "[num_machines: 1]",
+        "[local_listen_port: 12400]",
+        "[time_out: 120]",
+        "[machine_list_filename: ]",
+        "[machines: ]",
+        "[gpu_platform_id: -1]",
+        "[gpu_device_id: -1]",
+        "[num_gpu: 1]",
+    ]
+    all_param_entries = non_default_param_entries + default_param_entries
+
+    # add device-specific entries
+    #
+    # passed-in force_col_wise / force_row_wise parameters are ignored on CUDA and GPU builds...
+    # https://github.com/microsoft/LightGBM/blob/1d7ee63686272bceffd522284127573b511df6be/src/io/config.cpp#L375-L377
+    if getenv('TASK', '') == 'cuda':
+        device_entries = [
+            "[force_col_wise: 0]",
+            "[force_row_wise: 1]",
+            "[device_type: cuda]",
+            "[gpu_use_dp: 1]"
+        ]
+    elif getenv('TASK', '') == 'gpu':
+        device_entries = [
+            "[force_col_wise: 1]",
+            "[force_row_wise: 0]",
+            "[device_type: gpu]",
+            "[gpu_use_dp: 0]"
+        ]
+    else:
+        device_entries = [
+            "[force_col_wise: 0]",
+            "[force_row_wise: 0]",
+            "[device_type: cpu]",
+            "[gpu_use_dp: 0]"
+        ]
+
+    all_param_entries += device_entries
+
+    # check that model text has all expected param entries
+    for param_str in all_param_entries:
+        assert param_str in model_txt_from_file
+        assert param_str in model_txt_from_memory
+
+    # since Booster.model_to_string() is used when pickling, check that parameters all
+    # roundtrip pickling successfully too
+    gbm_pkl = pickle_and_unpickle_object(gbm, serializer="joblib")
+    model_txt_from_memory = gbm_pkl.model_to_string()
+    model_file = tmp_path / "out-pkl.model"
+    gbm_pkl.save_model(filename=model_file)
+    with open(model_file, "r") as f:
+        model_txt_from_file = f.read()
+
+    for param_str in all_param_entries:
+        assert param_str in model_txt_from_file
+        assert param_str in model_txt_from_memory
+
+
 def test_pandas_categorical():
     pd = pytest.importorskip("pandas")
     np.random.seed(42)  # sometimes there is no difference how cols are treated (cat or not cat)