diff --git a/tensorflow_probability/python/distributions/joint_distribution_coroutine.py b/tensorflow_probability/python/distributions/joint_distribution_coroutine.py
index cef887b10c..d5b442fd2c 100644
--- a/tensorflow_probability/python/distributions/joint_distribution_coroutine.py
+++ b/tensorflow_probability/python/distributions/joint_distribution_coroutine.py
@@ -219,8 +219,10 @@ def __init__(
     """
     parameters = dict(locals())
     with tf.name_scope(name or 'JointDistributionCoroutine') as name:
-      self._sample_dtype = sample_dtype
       self._model_coroutine = model
+      # Hint `no_dependency` to tell tf.Module not to screw up the sample dtype
+      # with extraneous wrapping (list => ListWrapper, etc.).
+      self._sample_dtype = self._no_dependency(sample_dtype)
       self._single_sample_distributions = {}
       super(JointDistributionCoroutine, self).__init__(
           dtype=sample_dtype,
diff --git a/tensorflow_probability/python/distributions/joint_distribution_coroutine_test.py b/tensorflow_probability/python/distributions/joint_distribution_coroutine_test.py
index 4aca2df9d8..5079ae8ebc 100644
--- a/tensorflow_probability/python/distributions/joint_distribution_coroutine_test.py
+++ b/tensorflow_probability/python/distributions/joint_distribution_coroutine_test.py
@@ -624,6 +624,7 @@ def noncentered_horseshoe_prior(num_features):
           tfd.Sample(tfd.Normal(0., 1.), num_features))
       yield tfd.Independent(tfd.Deterministic(weights_noncentered * scale),
                             reinterpreted_batch_ndims=1)
+
     # Currently sample_dtype is only used for `tf.nest.pack_structure_as`. In
     # the future we may use it for error checking and/or casting.
     sample_dtype = collections.namedtuple('Model', [
@@ -645,6 +646,18 @@ def noncentered_horseshoe_prior(num_features):
     self.assertEqual([3, 4], joint.log_prob(
         joint.sample([3, 4], seed=test_util.test_seed())).shape)
 
+    # Check that a list dtype doesn't get corrupted by `tf.Module` wrapping.
+    sample_dtype = [None, None, None, None]
+    joint = tfd.JointDistributionCoroutine(
+        lambda: noncentered_horseshoe_prior(4),
+        sample_dtype=sample_dtype,
+        validate_args=True)
+    ds, xs = joint.sample_distributions([2, 3], seed=test_util.test_seed())
+    self.assertEqual(type(sample_dtype), type(xs))
+    self.assertEqual(type(sample_dtype), type(ds))
+    tf.nest.assert_same_structure(sample_dtype, ds)
+    tf.nest.assert_same_structure(sample_dtype, xs)
+
   def test_repr_with_custom_sample_dtype(self):
     def model():
       s = yield tfd.JointDistributionCoroutine.Root(