diff --git a/spacy/tests/training/test_rehearse.py b/spacy/tests/training/test_rehearse.py
index 5ac7fc217..432047a73 100644
--- a/spacy/tests/training/test_rehearse.py
+++ b/spacy/tests/training/test_rehearse.py
@@ -1,6 +1,8 @@
 import pytest
 import spacy
 
+from thinc.api import Config
+
 from typing import List
 from spacy.training import Example
 
@@ -148,6 +150,157 @@ REHEARSE_DATA = [
     ),
 ]
 
+TEXTCAT_MULTILABEL_LISTENER_CONFIG = """
+[nlp]
+lang = "en"
+pipeline = ["tok2vec","textcat_multilabel"]
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+batch_size = 1000
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.textcat_multilabel]
+factory = "textcat_multilabel"
+threshold = 0.5
+
+[components.textcat_multilabel.model]
+@architectures = "spacy.TextCatEnsemble.v2"
+nO = null
+
+[components.textcat_multilabel.model.linear_model]
+@architectures = "spacy.TextCatBOW.v2"
+exclusive_classes = false
+ngram_size = 1
+no_output_layer = false 
+
+[components.textcat_multilabel.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = 64
+upstream = "*"
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 64
+attrs = ["ORTH", "SHAPE"]
+rows = [5000, 2500]
+include_static_vectors = true 
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MishWindowEncoder.v2"
+width = 64
+depth = 4
+window_size = 1
+"""
+
+TEXTCAT_LISTENER_CONFIG = """
+[nlp]
+lang = "en"
+pipeline = ["tok2vec","textcat"]
+batch_size = 1000
+
+[components]
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = ${components.tok2vec.model.encode.width}
+attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
+rows = [5000, 1000, 2500, 2500]
+include_static_vectors = true
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 256
+depth = 8
+window_size = 1
+maxout_pieces = 3
+
+[components.textcat]
+factory = "textcat"
+
+[components.textcat.model]
+@architectures = "spacy.TextCatEnsemble.v2"
+nO = null
+
+[components.textcat.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+
+[components.textcat.model.linear_model]
+@architectures = "spacy.TextCatBOW.v2"
+exclusive_classes = true
+ngram_size = 1
+no_output_layer = false
+"""
+
+TEXTCAT_CONFIG = """
+[nlp]
+lang = "en"
+pipeline = ["textcat"]
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+batch_size = 1000
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.textcat]
+factory = "textcat"
+threshold = 0.5
+
+[components.textcat.model]
+@architectures = "spacy.TextCatEnsemble.v2"
+nO = null
+
+[components.textcat.model.linear_model]
+@architectures = "spacy.TextCatBOW.v1"
+exclusive_classes = true
+ngram_size = 1
+no_output_layer = false
+nO = null
+
+[components.textcat.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.textcat.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v1"
+width = 64
+rows = [2000,2000,1000,1000,1000,1000]
+attrs = ["ORTH","LOWER","PREFIX","SUFFIX","SHAPE","ID"]
+include_static_vectors = false
+
+[components.textcat.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 64
+window_size = 1
+maxout_pieces = 3
+depth = 2
+"""
+
+TEXTCAT_EXAMPLE_TEXTS = [
+    ("This is a sentence for LABEL_A.", {"cats": {"LABEL_A": 1, "LABEL_B": 0}}),
+    ("A sentence for the label LABEL_B.", {"cats": {"LABEL_A": 0, "LABEL_B": 1}}),
+]
+
+TEXTCAT_LABELS = ["LABEL_A", "LABEL_B"]
+
 
 def _add_ner_label(ner, data):
     for _, annotations in data:
@@ -209,3 +362,63 @@ def test_rehearse(component):
     nlp.add_pipe(component)
     nlp = _optimize(nlp, component, TRAIN_DATA, False)
     _optimize(nlp, component, REHEARSE_DATA, True)
+
+
+@pytest.mark.issue(12044)
+def test_rehearse_textcat_multilabel_listener():
+    """Test nlp.rehearse on a textcat_multilabel pipeline with a tok2vec listener"""
+    config = Config().from_str(TEXTCAT_MULTILABEL_LISTENER_CONFIG)
+    nlp = spacy.blank("en").from_config(config)
+    textcat_multilabel = nlp.get_pipe("textcat_multilabel")
+    for label in TEXTCAT_LABELS:
+        textcat_multilabel.add_label(label)
+    nlp.initialize()
+
+    examples = []
+    for example in TEXTCAT_EXAMPLE_TEXTS:
+        example = Example.from_dict(nlp.make_doc(example[0]), example[1])
+        examples.append(example)
+        nlp.update([example])
+
+    optimizer = nlp.resume_training()
+    nlp.rehearse(examples, sgd=optimizer)
+
+
+@pytest.mark.issue(12044)
+def test_rehearse_textcat_listener():
+    """Test nlp.rehearse on a textcat pipeline with a tok2vec listener"""
+    config = Config().from_str(TEXTCAT_LISTENER_CONFIG)
+    nlp = spacy.blank("en").from_config(config)
+    textcat = nlp.get_pipe("textcat")
+    for label in TEXTCAT_LABELS:
+        textcat.add_label(label)
+    nlp.initialize()
+
+    examples = []
+    for example in TEXTCAT_EXAMPLE_TEXTS:
+        example = Example.from_dict(nlp.make_doc(example[0]), example[1])
+        examples.append(example)
+        nlp.update([example])
+
+    optimizer = nlp.resume_training()
+    nlp.rehearse(examples, sgd=optimizer)
+
+
+@pytest.mark.issue(12044)
+def test_rehearse_textcat():
+    """Test nlp.rehearse on a textcat pipeline with an inline tok2vec component"""
+    config = Config().from_str(TEXTCAT_CONFIG)
+    nlp = spacy.blank("en").from_config(config)
+    textcat = nlp.get_pipe("textcat")
+    for label in TEXTCAT_LABELS:
+        textcat.add_label(label)
+    nlp.initialize()
+
+    examples = []
+    for example in TEXTCAT_EXAMPLE_TEXTS:
+        example = Example.from_dict(nlp.make_doc(example[0]), example[1])
+        examples.append(example)
+        nlp.update([example])
+
+    optimizer = nlp.resume_training()
+    nlp.rehearse(examples, sgd=optimizer)