diff --git a/tests/test_metalearner.py b/tests/test_metalearner.py
index 0951aba..13d55f9 100644
--- a/tests/test_metalearner.py
+++ b/tests/test_metalearner.py
@@ -100,8 +100,6 @@ def predict_conditional_average_outcomes(self, X, is_oos, oos_method=None):
         return np.zeros((len(X), 2, 1))
 
 
-@pytest.mark.parametrize("nuisance_model_factory", [LGBMRegressor])
-@pytest.mark.parametrize("treatment_model_factory", [LGBMRegressor])
 @pytest.mark.parametrize("is_classification", [True, False])
 @pytest.mark.parametrize("nuisance_model_params", [None, {}, {"n_estimators": 5}])
 @pytest.mark.parametrize("treatment_model_params", [None, {}, {"n_estimators": 5}])
@@ -110,24 +108,36 @@ def predict_conditional_average_outcomes(self, X, is_oos, oos_method=None):
     [
         None,
         {
-            "nuisance1": ["X1"],
-            "nuisance2": ["X2"],
-            "propensity_model": ["Xp"],
-            "treatment1": ["X1"],
-            "treatment2": ["X2"],
+            VARIANT_OUTCOME_MODEL: ["X1"],
+            CONTROL_EFFECT_MODEL: ["X2"],
+            TREATMENT_EFFECT_MODEL: ["X1"],
+            TREATMENT_MODEL: ["X2"],
+            PROPENSITY_MODEL: ["Xp"],
+            OUTCOME_MODEL: ["X1"],
         },
     ],
 )
 @pytest.mark.parametrize(
-    "n_folds", [5, {"nuisance1": 1, "nuisance2": 1, "treatment1": 5, "treatment2": 10}]
+    "n_folds",
+    [
+        5,
+        {
+            VARIANT_OUTCOME_MODEL: 5,
+            CONTROL_EFFECT_MODEL: 5,
+            TREATMENT_EFFECT_MODEL: 5,
+            TREATMENT_MODEL: 5,
+            PROPENSITY_MODEL: 5,
+            OUTCOME_MODEL: 5,
+        },
+    ],
 )
-@pytest.mark.parametrize("propensity_model_factory", [None, LGBMClassifier])
 @pytest.mark.parametrize("propensity_model_params", [None, {}, {"n_estimators": 5}])
 @pytest.mark.parametrize("n_variants", [2, 5, 10])
+@pytest.mark.parametrize(
+    "implementation",
+    [TLearner, XLearner, RLearner, DRLearner],
+)
 def test_metalearner_init(
-    nuisance_model_factory,
-    treatment_model_factory,
-    propensity_model_factory,
     is_classification,
     n_variants,
     nuisance_model_params,
@@ -135,8 +145,12 @@ def test_metalearner_init(
     propensity_model_params,
     feature_set,
     n_folds,
+    implementation,
 ):
-    _TestMetaLearner(
+    propensity_model_factory = LGBMClassifier
+    nuisance_model_factory = LGBMClassifier if is_classification else LGBMRegressor
+    treatment_model_factory = LGBMRegressor
+    model = implementation(
         nuisance_model_factory=nuisance_model_factory,
         is_classification=is_classification,
         n_variants=n_variants,
@@ -148,6 +162,12 @@ def test_metalearner_init(
         feature_set=feature_set,
         n_folds=n_folds,
     )
+    all_base_models = set(model.nuisance_model_specifications().keys()) | set(
+        model.treatment_model_specifications().keys()
+    )
+    assert set(model.n_folds.keys()) == all_base_models
+    assert all(isinstance(n_fold, int) for n_fold in model.n_folds.values())
+    assert set(model.feature_set.keys()) == all_base_models
 
 
 @pytest.mark.parametrize(