diff --git a/spacy/tests/regression/test_issue5501-6000.py b/spacy/tests/regression/test_issue5501-6000.py
index f0b46cb83..65fc8dda7 100644
--- a/spacy/tests/regression/test_issue5501-6000.py
+++ b/spacy/tests/regression/test_issue5501-6000.py
@@ -1,35 +1,38 @@
-from thinc.api import fix_random_seed
+import pytest
+from thinc.api import Config, fix_random_seed
+
 from spacy.lang.en import English
+from spacy.pipeline.textcat import default_model_config, bow_model_config
+from spacy.pipeline.textcat import cnn_model_config
 from spacy.tokens import Span
 from spacy import displacy
 from spacy.pipeline import merge_entities
+from spacy.training import Example
 
 
-def test_issue5551():
+@pytest.mark.parametrize(
+    "textcat_config", [default_model_config, bow_model_config, cnn_model_config]
+)
+def test_issue5551(textcat_config):
     """Test that after fixing the random seed, the results of the pipeline are truly identical"""
     component = "textcat"
-    pipe_cfg = {
-        "model": {
-            "@architectures": "spacy.TextCatBOW.v1",
-            "exclusive_classes": True,
-            "ngram_size": 2,
-            "no_output_layer": False,
-        }
-    }
+
+    pipe_cfg = Config().from_str(textcat_config)
     results = []
     for i in range(3):
         fix_random_seed(0)
         nlp = English()
-        example = (
-            "Once hot, form ping-pong-ball-sized balls of the mixture, each weighing roughly 25 g.",
-            {"cats": {"Labe1": 1.0, "Label2": 0.0, "Label3": 0.0}},
-        )
+        text = "Once hot, form ping-pong-ball-sized balls of the mixture, each weighing roughly 25 g."
+        annots = {"cats": {"Labe1": 1.0, "Label2": 0.0, "Label3": 0.0}}
         pipe = nlp.add_pipe(component, config=pipe_cfg, last=True)
-        for label in set(example[1]["cats"]):
+        for label in set(annots["cats"]):
             pipe.add_label(label)
+        # Train
         nlp.initialize()
+        doc = nlp.make_doc(text)
+        nlp.update([Example.from_dict(doc, annots)])
         # Store the result of each iteration
-        result = pipe.model.predict([nlp.make_doc(example[0])])
+        result = pipe.model.predict([doc])
         results.append(list(result[0]))
     # All results should be the same because of the fixed seed
     assert len(results) == 3
diff --git a/spacy/tests/training/test_readers.py b/spacy/tests/training/test_readers.py
index ff2559d2a..5669bdd11 100644
--- a/spacy/tests/training/test_readers.py
+++ b/spacy/tests/training/test_readers.py
@@ -72,6 +72,10 @@ def test_readers():
 def test_cat_readers(reader, additional_config):
     nlp_config_string = """
     [training]
+    seed = 0
+    
+    [training.score_weights]
+    cats_macro_auc = 1.0
 
     [corpora]
     @readers = "PLACEHOLDER"
@@ -92,9 +96,7 @@ def test_cat_readers(reader, additional_config):
     config["corpora"]["@readers"] = reader
     config["corpora"].update(additional_config)
     nlp = load_model_from_config(config, auto_fill=True)
-    T = registry.resolve(
-        nlp.config["training"].interpolate(), schema=ConfigSchemaTraining
-    )
+    T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
     dot_names = [T["train_corpus"], T["dev_corpus"]]
     train_corpus, dev_corpus = resolve_dot_names(nlp.config, dot_names)
     optimizer = T["optimizer"]