diff --git a/conftest.py b/conftest.py
index c4fa713c72..f54e5b9efc 100644
--- a/conftest.py
+++ b/conftest.py
@@ -145,21 +145,25 @@ def pytest_sessionfinish(session: pytest.Session, exitstatus):  # pylint: disabl
 
 @pytest.fixture
 def default_configuration():
-    """Return the default test compilation configuration."""
+    # Return the default test compilation configuration.
+    def default_configuration_impl(mode: Optional[str] = "MULTI"):
+        if mode == "MULTI":
+            parameter_selection_strategy = ParameterSelectionStrategy.MULTI
+            single_precision = False
+        else:
+            parameter_selection_strategy = ParameterSelectionStrategy.MONO
+            single_precision = True
+
+        return Configuration(
+            dump_artifacts_on_unexpected_failures=False,
+            enable_unsafe_features=True,
+            use_insecure_key_cache=True,
+            insecure_key_cache_location="ConcreteNumpyKeyCache",
+            parameter_selection_strategy=parameter_selection_strategy,
+            single_precision=single_precision,
+        )
 
-    # Remove parameter_selection_strategy once it is set to multi-parameter in Concrete Python
-    # by default
-    # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3860
-    # Parameter `enable_unsafe_features` and `use_insecure_key_cache` are needed in order to be
-    # able to cache generated keys through `insecure_key_cache_location`. As the name suggests,
-    # these parameters are unsafe and should only be used for debugging in development
-    return Configuration(
-        dump_artifacts_on_unexpected_failures=False,
-        enable_unsafe_features=True,
-        use_insecure_key_cache=True,
-        insecure_key_cache_location="ConcreteNumpyKeyCache",
-        parameter_selection_strategy=ParameterSelectionStrategy.MULTI,
-    )
+    return default_configuration_impl
 
 
 REMOVE_COLOR_CODES_RE = re.compile(r"\x1b[^m]*m")
diff --git a/src/concrete/ml/pytest/utils.py b/src/concrete/ml/pytest/utils.py
index 3bef4b8a13..ba36f4cf66 100644
--- a/src/concrete/ml/pytest/utils.py
+++ b/src/concrete/ml/pytest/utils.py
@@ -103,8 +103,8 @@
     pytest.param(
         model,
         {
-            "n_samples": 10,
-            "n_features": 3,
+            "n_samples": 4,
+            "n_features": 2,
             "n_classes": n_classes,
             "n_informative": 2,
             "n_redundant": 0,
diff --git a/tests/common/test_pbs_error_probability_settings.py b/tests/common/test_pbs_error_probability_settings.py
index 4066119eb9..507b570afa 100644
--- a/tests/common/test_pbs_error_probability_settings.py
+++ b/tests/common/test_pbs_error_probability_settings.py
@@ -41,24 +41,17 @@ def test_config_sklearn(model_class, parameters, kwargs, load_data, default_conf
         # Fit the model
         model.fit(x, y)
 
-    if get_model_name(model_class) == "KNeighborsClassifier":
-
-        default_configuration = Configuration(
-            dump_artifacts_on_unexpected_failures=False,
-            enable_unsafe_features=True,
-            use_insecure_key_cache=True,
-            insecure_key_cache_location="ConcreteNumpyKeyCache",
-            parameter_selection_strategy=fhe.ParameterSelectionStrategy.MONO,
-            single_precision=True,
-        )
+    # KNN works only for MONO in the latest concrete Python version
+    # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3978
+    mode = "MONO" if get_model_name(model) == "KNeighborsClassifier" else "MULTI"
 
     if kwargs.get("p_error", None) is not None and kwargs.get("global_p_error", None) is not None:
         with pytest.raises(ValueError) as excinfo:
-            model.compile(x, default_configuration, verbose=True, **kwargs)
+            model.compile(x, default_configuration(mode), verbose=True, **kwargs)
         assert "Please only set one of (p_error, global_p_error) values" in str(excinfo.value)
     else:
 
-        model.compile(x, default_configuration, verbose=True, **kwargs)
+        model.compile(x, default_configuration(mode), verbose=True, **kwargs)
 
     # We still need to check that we have the expected probabilities
     # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/2206
diff --git a/tests/deployment/test_client_server.py b/tests/deployment/test_client_server.py
index f5e4a8e438..d748d39fac 100644
--- a/tests/deployment/test_client_server.py
+++ b/tests/deployment/test_client_server.py
@@ -97,24 +97,17 @@ def test_client_server_sklearn(
 
     # Compile
     extra_params = {"global_p_error": 1 / 100_000}
-
-    if get_model_name(model_class) == "KNeighborsClassifier":
-
-        default_configuration = Configuration(
-            dump_artifacts_on_unexpected_failures=False,
-            enable_unsafe_features=True,
-            use_insecure_key_cache=True,
-            insecure_key_cache_location="ConcreteNumpyKeyCache",
-            parameter_selection_strategy=fhe.ParameterSelectionStrategy.MONO,
-            single_precision=True,
-        )
+    # KNN works only for MONO in the latest concrete Python version
+    # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3978
+    mode = "MONO" if get_model_name(model) == "KNeighborsClassifier" else "MULTI"
 
     # Running the simulation using a model that is not compiled should not be possible
     with pytest.raises(AttributeError, match=".* model is not compiled.*"):
-        client_server_simulation(x_train, x_test, model, default_configuration)
+        client_server_simulation(x_train, x_test, model, default_configuration(mode))
+
     # With n_bits = 3, KNN is not compilable
     fhe_circuit = model.compile(
-        x_train, default_configuration, **extra_params, show_mlir=(n_bits <= 8)
+        x_train, default_configuration(mode), **extra_params, show_mlir=(n_bits <= 8)
     )
     max_bit_width = fhe_circuit.graph.maximum_integer_bit_width()
     print(f"Max width {max_bit_width}")
@@ -122,7 +115,7 @@ def test_client_server_sklearn(
     # Check that the FHE execution is correct.
     # With a global_p_error of 1/100_000 we only allow one run.
     check_is_good_execution_for_cml_vs_circuit(x_test, model, simulate=False, n_allowed_runs=1)
-    client_server_simulation(x_train, x_test, model, default_configuration)
+    client_server_simulation(x_train, x_test, model, default_configuration(mode))
 
 
 def test_client_server_custom_model(
@@ -138,7 +131,7 @@ def test_client_server_custom_model(
         # Instantiate an empty QuantizedModule object
         quantized_module = QuantizedModule()
 
-        client_server_simulation(x_train, x_test, quantized_module, default_configuration)
+        client_server_simulation(x_train, x_test, quantized_module, default_configuration("MULTI"))
 
     torch_model = FCSmall(2, nn.ReLU)
     n_bits = 2
@@ -147,7 +140,7 @@ def test_client_server_custom_model(
     quantized_numpy_module = compile_torch_model(
         torch_model,
         x_train,
-        configuration=default_configuration,
+        configuration=default_configuration("MULTI"),
         n_bits=n_bits,
         global_p_error=1 / 100_000,
     )
@@ -158,7 +151,7 @@ def test_client_server_custom_model(
         x_test, quantized_numpy_module, simulate=False, n_allowed_runs=1
     )
 
-    client_server_simulation(x_train, x_test, quantized_numpy_module, default_configuration)
+    client_server_simulation(x_train, x_test, quantized_numpy_module, default_configuration())
 
 
 def client_server_simulation(x_train, x_test, model, default_configuration):
diff --git a/tests/parameter_search/test_p_error_binary_search.py b/tests/parameter_search/test_p_error_binary_search.py
index 5ab3ffee61..a24479ce44 100644
--- a/tests/parameter_search/test_p_error_binary_search.py
+++ b/tests/parameter_search/test_p_error_binary_search.py
@@ -312,6 +312,7 @@ def test_binary_search_for_built_in_models(model_class, parameters, threshold, p
     # Skorch but since Scikit-Learn does not, we don't as well. This issue could be fixed by making
     # neural networks not inherit from Skorch.
     # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3373
+
     # Skipping predict_proba for KNN, doesn't work for now.
     # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3962
 
diff --git a/tests/quantization/test_compilation.py b/tests/quantization/test_compilation.py
index 20be11e4f9..664dd2fc64 100644
--- a/tests/quantization/test_compilation.py
+++ b/tests/quantization/test_compilation.py
@@ -78,7 +78,7 @@ def test_quantized_module_compilation(
     # Compile
     quantized_model.compile(
         numpy_input,
-        default_configuration,
+        default_configuration(),
         verbose=verbose,
     )
 
@@ -138,7 +138,7 @@ def test_quantized_cnn_compilation(
     # Compile
     quantized_model.compile(
         numpy_input,
-        default_configuration,
+        default_configuration(),
         verbose=verbose,
     )
     check_is_good_execution_for_cml_vs_circuit(numpy_input, quantized_model, simulate=simulate)
@@ -317,7 +317,7 @@ def test_compile_multi_input_nn_with_input_tlus(
     # Compile
     quantized_model.compile(
         numpy_input,
-        default_configuration,
+        default_configuration(),
     )
     check_is_good_execution_for_cml_vs_circuit(numpy_input, quantized_model, simulate=True)
 
diff --git a/tests/quantization/test_quantized_module.py b/tests/quantization/test_quantized_module.py
index 49072a2f50..c1ae8fa1d1 100644
--- a/tests/quantization/test_quantized_module.py
+++ b/tests/quantization/test_quantized_module.py
@@ -246,7 +246,7 @@ def test_bitwidth_report(model_class, input_shape, activation_function, default_
         torch_fc_model,
         torch_input,
         False,
-        default_configuration,
+        default_configuration(),
         n_bits=2,
         p_error=0.01,
     )
@@ -336,7 +336,7 @@ def test_quantized_module_rounding_fhe(model_class, input_shape, default_configu
         torch_fc_model,
         torch_input,
         False,
-        default_configuration,
+        default_configuration(),
         n_bits=2,
         p_error=0.01,
         rounding_threshold_bits=6,
diff --git a/tests/seeding/test_seeding.py b/tests/seeding/test_seeding.py
index 27af16c06e..7d54f962c4 100644
--- a/tests/seeding/test_seeding.py
+++ b/tests/seeding/test_seeding.py
@@ -106,7 +106,7 @@ def test_seed_sklearn(model_class, parameters, load_data, default_configuration)
 
     # Test the determinism of our package (even if the bit-width may be too large)
     try:
-        model.compile(x, configuration=default_configuration, show_mlir=True)
+        model.compile(x, configuration=default_configuration("MULTI"), show_mlir=True)
     except RuntimeError as err:
         print(err)
     except AssertionError as err:
diff --git a/tests/sklearn/test_dump_onnx.py b/tests/sklearn/test_dump_onnx.py
index f1949a6ca3..4967dea155 100644
--- a/tests/sklearn/test_dump_onnx.py
+++ b/tests/sklearn/test_dump_onnx.py
@@ -37,9 +37,15 @@ def check_onnx_file_dump(model_class, parameters, load_data, str_expected, defau
         model.set_params(**model_params)
 
     if get_model_name(model) == "KNeighborsClassifier":
-        model.n_bits = 4
-        default_configuration.parameter_selection_strategy = fhe.ParameterSelectionStrategy.MONO
-        default_configuration.single_precision = True
+        # KNN works only for MONO in the latest concrete Python version
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3978
+        mode = "MONO"
+        # KNN works only for small quantization bits
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3979
+        model.n_bits = 2
+
+    else:
+        mode = "MULTI"
 
     with warnings.catch_warnings():
         # Sometimes, we miss convergence, which is not a problem for our test
@@ -49,7 +55,7 @@ def check_onnx_file_dump(model_class, parameters, load_data, str_expected, defau
 
     with warnings.catch_warnings():
         # Use FHE simulation to not have issues with precision
-        model.compile(x, default_configuration)
+        model.compile(x, default_configuration(mode))
     # Get ONNX model
     onnx_model = model.onnx_model
 
@@ -423,7 +429,7 @@ def test_dump(
   return %variable
 }""",
         "KNeighborsClassifier": """graph torch_jit (
-  %input_0[DOUBLE, symx3]
+  %input_0[DOUBLE, symx2]
 ) {
   %/_operators.0/Constant_output_0 = Constant[value = <Tensor>]()
   %/_operators.0/Unsqueeze_output_0 = Unsqueeze(%input_0, %/_operators.0/Constant_output_0)
diff --git a/tests/sklearn/test_qnn.py b/tests/sklearn/test_qnn.py
index f164acb388..884fd65e7b 100644
--- a/tests/sklearn/test_qnn.py
+++ b/tests/sklearn/test_qnn.py
@@ -224,7 +224,7 @@ def test_compile_and_calib(
     # Compile the model
     model.compile(
         x_train,
-        configuration=default_configuration,
+        configuration=default_configuration("MULTI"),
     )
 
     # Execute in FHE, but don't check the value.
@@ -406,7 +406,7 @@ def _get_number_of_neurons(module: SparseQuantNeuralNetwork):
     # Compile the model
     model.compile(
         x_train,
-        configuration=default_configuration,
+        configuration=default_configuration("MULTI"),
     )
 
     pruned_model = model.prune(x_train, y_train, 0.5)
@@ -415,7 +415,7 @@ def _get_number_of_neurons(module: SparseQuantNeuralNetwork):
     # Compile the pruned model, this will also perform ONNX export and calibration
     pruned_model.compile(
         x_train,
-        configuration=default_configuration,
+        configuration=default_configuration("MULTI"),
     )
 
     with pytest.raises(
@@ -592,7 +592,7 @@ def test_power_of_two_scaling(
         # in compilation
         model.compile(
             x_train,
-            configuration=default_configuration,
+            configuration=default_configuration("MULTI"),
         )
 
         # Compute the results with simulation, which uses the actual
diff --git a/tests/sklearn/test_sklearn_models.py b/tests/sklearn/test_sklearn_models.py
index 931d0c3222..76a395db1d 100644
--- a/tests/sklearn/test_sklearn_models.py
+++ b/tests/sklearn/test_sklearn_models.py
@@ -656,8 +656,8 @@ def check_pipeline(model_class, x, y):
         param_grid = {
             "model__n_bits": [2, 3],
         }
-
-    grid_search = GridSearchCV(pipe_cv, param_grid, error_score="raise", cv=3)
+    # Since the data-set is really small for KNN, we have to decrease the number of splits
+    grid_search = GridSearchCV(pipe_cv, param_grid, error_score="raise", cv=2)
 
     # Sometimes, we miss convergence, which is not a problem for our test
     with warnings.catch_warnings():
@@ -686,9 +686,7 @@ def check_grid_search(model_class, x, y, scoring):
             "n_jobs": [1],
         }
     elif model_class in get_sklearn_neighbors_models():
-        param_grid = {
-            "n_bits": [3],
-        }
+        param_grid = {"n_bits": [2], "n_neighbors": [2]}
     else:
         param_grid = {
             "n_bits": [20],
@@ -706,8 +704,11 @@ def check_grid_search(model_class, x, y, scoring):
             # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3962
             pytest.skip("Skipping predict_proba for KNN, doesn't work for now")
 
+        # pylint: disable=invalid-name
+        cv = 2 if get_model_name(model_class) == "KNeighborsClassifier" else 5
+
         _ = GridSearchCV(
-            model_class(), param_grid, cv=5, scoring=scoring, error_score="raise", n_jobs=1
+            model_class(), param_grid, cv=cv, scoring=scoring, error_score="raise", n_jobs=1
         ).fit(x, y)
 
 
@@ -1074,14 +1075,12 @@ def check_exposition_structural_methods_decision_trees(model, x, y):
 def check_mono_parameter_warnings(model, x, default_configuration):
     """Check that setting voluntarily a mono-parameter strategy properly raises a warning."""
 
-    # Set the parameter strategy to mono-parameter
-    default_configuration.parameter_selection_strategy = ParameterSelectionStrategy.MONO
-
     with pytest.warns(
         UserWarning,
         match="Setting the parameter_selection_strategy to mono-parameter is not recommended.*",
     ):
-        model.compile(x, default_configuration)
+        # Set the parameter strategy to mono-parameter
+        model.compile(x, default_configuration(mode="MONO"))
 
 
 @pytest.mark.parametrize("model_class, parameters", sklearn_models_and_datasets)
@@ -1270,7 +1269,7 @@ def test_serialization(
     model, x = preamble(model_class, parameters, n_bits, load_data, is_weekly_option)
 
     # Compile the model to make sure we consider all possible attributes during the serialization
-    model.compile(x, default_configuration)
+    model.compile(x, default_configuration("MULTI"))
 
     if verbose:
         print("Run check_serialization")
@@ -1353,7 +1352,7 @@ def test_input_support(
     if verbose:
         print("Run input_support")
 
-    check_input_support(model_class, n_bits, default_configuration, x, y, input_type)
+    check_input_support(model_class, n_bits, default_configuration("MULTI"), x, y, input_type)
 
 
 @pytest.mark.parametrize("model_class, parameters", sklearn_models_and_datasets)
@@ -1475,14 +1474,13 @@ def test_predict_correctness(
                 print("Compile the model")
 
             with warnings.catch_warnings():
+                # KNN works only for MONO in the latest concrete Python version
+                # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3978
+                mode = "MONO" if get_model_name(model) == "KNeighborsClassifier" else "MULTI"
 
-                if get_model_name(model) == "KNeighborsClassifier":
-                    default_configuration.parameter_selection_strategy = (
-                        ParameterSelectionStrategy.MONO
-                    )
                 fhe_circuit = model.compile(
                     x,
-                    default_configuration,
+                    default_configuration(mode),
                     show_mlir=verbose and (n_bits <= 8),
                 )
 
@@ -1567,13 +1565,18 @@ def test_p_error_global_p_error_simulation(
     if "global_p_error" in error_param:
         pytest.skip("global_p_error behave very differently depending on the type of model.")
 
-    # Get data-set
-    n_bits = min(N_BITS_REGULAR_BUILDS)
     if get_model_name(model_class) == "KNeighborsClassifier":
-        n_bits = min(n_bits, 2)
-        default_configuration.parameter_selection_strategy = ParameterSelectionStrategy.MONO
+        # KNN works only for MONO in the latest concrete Python version
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3978
+        mode = "MONO"
+        # KNN works only for smaller quantization bits
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3979
+        n_bits = min([2] + N_BITS_REGULAR_BUILDS)
+    else:
+        mode = "MULTI"
+        n_bits = min(N_BITS_REGULAR_BUILDS)
 
-    # Initialize and fit the model
+    # Get data-set, initialize and fit the model
     model, x = preamble(model_class, parameters, n_bits, load_data, is_weekly_option)
 
     # Check if model is linear
@@ -1583,7 +1586,7 @@ def test_p_error_global_p_error_simulation(
     is_knn_model = is_model_class_in_a_list(model_class, get_sklearn_neighbors_models())
 
     # Compile with a large p_error to be sure the result is random.
-    model.compile(x, default_configuration, **error_param)
+    model.compile(x, default_configuration(mode), **error_param)
 
     def check_for_divergent_predictions(x, model, fhe, max_iterations=N_ALLOWED_FHE_RUN):
         """Detect divergence between simulated/FHE execution and clear run."""
diff --git a/tests/torch/test_brevitas_qat.py b/tests/torch/test_brevitas_qat.py
index 72e46d09ab..4db09ec875 100644
--- a/tests/torch/test_brevitas_qat.py
+++ b/tests/torch/test_brevitas_qat.py
@@ -222,7 +222,7 @@ def test_with_concrete(quantized_module, test_loader, use_fhe_simulation):
     q_module_simulated = compile_brevitas_qat_model(
         net,
         x_all,
-        configuration=default_configuration,
+        configuration=default_configuration("MULTI"),
     )
 
     fhe_s_correct = test_with_concrete(
@@ -491,7 +491,7 @@ def test_brevitas_constant_folding(default_configuration):
         compile_brevitas_qat_model(
             model.to("cpu"),
             torch_inputset=data,
-            configuration=default_configuration,
+            configuration=default_configuration("MULTI"),
         )
 
 
@@ -522,7 +522,7 @@ def test_brevitas_power_of_two(
     quantized_module = compile_brevitas_qat_model(
         net.to("cpu"),
         torch_inputset=x_all,
-        configuration=default_configuration,
+        configuration=default_configuration("MULTI"),
         rounding_threshold_bits=manual_rounding,
     )
 
diff --git a/tests/torch/test_compile_keras.py b/tests/torch/test_compile_keras.py
index 8bf4b065b3..6eafdbd06a 100644
--- a/tests/torch/test_compile_keras.py
+++ b/tests/torch/test_compile_keras.py
@@ -119,7 +119,7 @@ def test_compile_keras_networks(
         input_output_feature,
         model,
         OPSET_VERSION_FOR_ONNX_EXPORT,
-        default_configuration,
+        default_configuration("MULTI"),
         simulate,
         check_is_good_execution_for_cml_vs_circuit,
     )
diff --git a/tests/torch/test_compile_torch.py b/tests/torch/test_compile_torch.py
index be5f50af61..d4568be017 100644
--- a/tests/torch/test_compile_torch.py
+++ b/tests/torch/test_compile_torch.py
@@ -479,7 +479,7 @@ def test_compile_torch_or_onnx_networks(
         model_class=model,
         activation_function=activation_function,
         qat_bits=qat_bits,
-        default_configuration=default_configuration,
+        default_configuration=default_configuration("MULTI"),
         simulate=simulate,
         is_onnx=is_onnx,
         check_is_good_execution_for_cml_vs_circuit=check_is_good_execution_for_cml_vs_circuit,
@@ -529,7 +529,7 @@ def test_compile_torch_or_onnx_conv_networks(  # pylint: disable=unused-argument
         model_class=model,
         activation_function=activation_function,
         qat_bits=qat_bits,
-        default_configuration=default_configuration,
+        default_configuration=default_configuration("MULTI"),
         simulate=simulate,
         is_onnx=is_onnx,
         check_is_good_execution_for_cml_vs_circuit=check_is_good_execution_for_cml_vs_circuit,
@@ -605,7 +605,7 @@ def test_compile_torch_or_onnx_activations(
         model,
         activation_function,
         qat_bits,
-        default_configuration,
+        default_configuration("MULTI"),
         simulate,
         is_onnx,
         check_is_good_execution_for_cml_vs_circuit,
@@ -649,7 +649,7 @@ def test_compile_torch_qat(
         model,
         nn.Sigmoid,
         qat_bits,
-        default_configuration,
+        default_configuration("MULTI"),
         simulate,
         is_onnx,
         check_is_good_execution_for_cml_vs_circuit,
@@ -692,7 +692,7 @@ def test_compile_brevitas_qat(
         model_class=model_class,
         activation_function=None,
         qat_bits=qat_bits,
-        default_configuration=default_configuration,
+        default_configuration=default_configuration("MULTI"),
         simulate=simulate,
         is_onnx=False,
         check_is_good_execution_for_cml_vs_circuit=check_is_good_execution_for_cml_vs_circuit,
@@ -764,7 +764,7 @@ def test_dump_torch_network(
         model_class,
         activation_function,
         qat_bits,
-        default_configuration,
+        default_configuration("MULTI"),
         simulate,
         is_onnx,
         check_is_good_execution_for_cml_vs_circuit,
@@ -907,7 +907,7 @@ def test_qat_import_bits_check(default_configuration):
     quantized_numpy_module = compile_brevitas_qat_model(
         model,
         inputset,
-        configuration=default_configuration,
+        configuration=default_configuration("MULTI"),
     )
 
     n_percent_inputset_examples_test = 0.1
@@ -925,7 +925,7 @@ def test_qat_import_bits_check(default_configuration):
             model,
             inputset,
             n_bits=n_bits,
-            configuration=default_configuration,
+            configuration=default_configuration("MULTI"),
         )
 
         new_predictions = quantized_numpy_module.forward(*x_test, fhe="disable")
@@ -968,7 +968,7 @@ def test_qat_import_check(default_configuration, check_is_good_execution_for_cml
             partial(SimpleQAT, n_bits=6, disable_bit_check=True),
             nn.ReLU,
             qat_bits,
-            default_configuration,
+            default_configuration("MULTI"),
             simulate,
             False,
             check_is_good_execution_for_cml_vs_circuit,
@@ -984,7 +984,7 @@ def test_qat_import_check(default_configuration, check_is_good_execution_for_cml
             CNNOther,
             nn.ReLU,
             qat_bits,
-            default_configuration,
+            default_configuration("MULTI"),
             simulate,
             False,
             check_is_good_execution_for_cml_vs_circuit,
@@ -1016,7 +1016,7 @@ def __init__(self, input_output, activation_function):
             AllZeroCNN,
             nn.ReLU,
             qat_bits,
-            default_configuration,
+            default_configuration("MULTI"),
             simulate,
             False,
             check_is_good_execution_for_cml_vs_circuit,
@@ -1095,7 +1095,7 @@ def decorate_name(self):
         quantized_numpy_module = compile_brevitas_qat_model(
             net,
             inputset,
-            configuration=default_configuration,
+            configuration=default_configuration("MULTI"),
         )
     else:
         # Compile with PTQ. Note that this will have zero-point>0
@@ -1103,7 +1103,7 @@ def decorate_name(self):
             net,
             inputset,
             import_qat=False,
-            configuration=default_configuration,
+            configuration=default_configuration("MULTI"),
             n_bits=n_bits,
         )
 
@@ -1136,14 +1136,14 @@ def test_shape_operations_net(
         quantized_module = compile_brevitas_qat_model(
             net,
             inputset,
-            configuration=default_configuration,
+            configuration=default_configuration("MULTI"),
             p_error=0.01,
         )
     else:
         quantized_module = compile_torch_model(
             net,
             inputset,
-            configuration=default_configuration,
+            configuration=default_configuration("MULTI"),
             n_bits=3,
             p_error=0.01,
         )
@@ -1184,7 +1184,7 @@ def test_torch_padding(default_configuration, check_circuit_has_no_tlu):
     quant_model = compile_brevitas_qat_model(
         net,
         inputset,
-        configuration=default_configuration,
+        configuration=default_configuration("MULTI"),
         p_error=0.01,
     )
 
@@ -1245,7 +1245,7 @@ def test_fancy_indexing_torch(model_object, default_configuration):
     """Test fancy indexing torch."""
     model = model_object(10, 10, 2, 4, 3)
     x = numpy.random.randint(0, 2, size=(100, 3, 10)).astype(numpy.float64)
-    compile_brevitas_qat_model(model, x, n_bits=4, configuration=default_configuration)
+    compile_brevitas_qat_model(model, x, n_bits=4, configuration=default_configuration("MULTI"))
 
 
 @pytest.mark.parametrize(
@@ -1267,9 +1267,6 @@ def test_mono_parameter_rounding_warning(
     # The QAT bits is set to 0 in order to signal that the network is not using QAT
     qat_bits = 0
 
-    # Set the parameter strategy to mono-parameter
-    default_configuration.parameter_selection_strategy = ParameterSelectionStrategy.MONO
-
     with pytest.warns(
         UserWarning,
         match=".* set the optimization strategy to multi-parameter when using rounding.*",
@@ -1279,7 +1276,8 @@ def test_mono_parameter_rounding_warning(
             model_class=model,
             activation_function=nn.ReLU,
             qat_bits=qat_bits,
-            default_configuration=default_configuration,
+            # Set the parameter strategy to mono-parameter
+            default_configuration=default_configuration("MONO"),
             simulate=True,
             is_onnx=is_onnx,
             check_is_good_execution_for_cml_vs_circuit=check_is_good_execution_for_cml_vs_circuit,
diff --git a/tests/torch/test_reduce_sum.py b/tests/torch/test_reduce_sum.py
index dca1f3b16d..de528fb6c6 100644
--- a/tests/torch/test_reduce_sum.py
+++ b/tests/torch/test_reduce_sum.py
@@ -75,7 +75,7 @@ def test_sum(
     quantized_module = compile_torch_model(
         torch_model,
         inputset,
-        configuration=default_configuration,
+        configuration=default_configuration("MULTI"),
         n_bits=n_bits,
     )
 
diff --git a/tests/virtual_lib/test_virtual_lib.py b/tests/virtual_lib/test_virtual_lib.py
index 47d73ebb55..c1aa92603e 100644
--- a/tests/virtual_lib/test_virtual_lib.py
+++ b/tests/virtual_lib/test_virtual_lib.py
@@ -18,7 +18,7 @@ def f(x, weights):
     inputset = [thousand_ones]
     fhe_simulation_circuit = matmul_thousand_ones_compiler.compile(
         inputset,
-        default_configuration,
+        default_configuration(),
     )
 
     assert isinstance(fhe_simulation_circuit, Circuit)
@@ -40,7 +40,7 @@ def f(x, weights):
     inputset = [numpy.ones((3000,), dtype=numpy.int64)]
     fhe_simulation_circuit = matmul_three_thousand_plus_minus_ones_compiler.compile(
         inputset,
-        default_configuration,
+        default_configuration(),
     )
 
     assert isinstance(fhe_simulation_circuit, Circuit)
@@ -62,7 +62,7 @@ def g(x, weights):
     inputset = [numpy.ones((3000,), dtype=numpy.int64)]
     fhe_simulation_circuit = sin_matmul_three_thousand_plus_minus_ones_compiler.compile(
         inputset,
-        default_configuration,
+        default_configuration(),
     )
 
     assert isinstance(fhe_simulation_circuit, Circuit)