diff --git a/flax/nnx/tests/transforms_test.py b/flax/nnx/tests/transforms_test.py
index 1d9c0b707b..93809deae4 100644
--- a/flax/nnx/tests/transforms_test.py
+++ b/flax/nnx/tests/transforms_test.py
@@ -15,6 +15,7 @@
 import dataclasses
 import typing as tp
 from functools import partial
+from absl.testing import parameterized
 
 import jax
 import jax.numpy as jnp
@@ -345,7 +346,8 @@ def constrain_object(m):
     m.kernel.value.sharding
 
 
-class TestGrad:
+
+class TestGrad(parameterized.TestCase):
   def test_grad(self):
     p1 = nnx.Param(10.0)
     p2 = nnx.Param(20.0)
@@ -451,34 +453,21 @@ def test_multiple_inputs(self):
     assert 'bias' in grads
     assert grads.bias.value.shape == (3,)
 
-  def test_multiple_graph_nodes(self):
-    rngs = nnx.Rngs(0)
-    m1 = nnx.Linear(2, 3, rngs=rngs)
-    m2 = nnx.Linear(3, 3, rngs=rngs)
-    loss_fn = lambda m1, m2, x, y: jnp.mean((m2(m1(x)) - y) ** 2)
-    grad_fn = nnx.grad(loss_fn, argnums=(0, 1), wrt=nnx.Param)
-    x = jax.random.uniform(rngs(), (1, 2))
-    y = jnp.ones((1, 3))
-    grads_m1, grads_m2 = grad_fn(m1, m2, x, y)
-
-    assert 'kernel' in grads_m1
-    assert grads_m1.kernel.value.shape == (2, 3)
-    assert 'bias' in grads_m1
-    assert grads_m1.bias.value.shape == (3,)
-    assert 'kernel' in grads_m2
-    assert grads_m2.kernel.value.shape == (3, 3)
-    assert 'bias' in grads_m2
-    assert grads_m2.bias.value.shape == (3,)
-
-  def test_multiple_graph_nodes_mix_positions(self):
+  @parameterized.parameters(
+    {'loss_fn': lambda m1, m2, x, y: jnp.mean((m2(m1(x)) - y) ** 2), 'argnums': (0, 1)},
+    {'loss_fn': lambda x, m1, y, m2: jnp.mean((m2(m1(x)) - y) ** 2), 'argnums': (1, 3)}
+  )
+  def test_multiple_graph_nodes(self, loss_fn, argnums):
     rngs = nnx.Rngs(0)
     m1 = nnx.Linear(2, 3, rngs=rngs)
     m2 = nnx.Linear(3, 3, rngs=rngs)
-    loss_fn = lambda x, m1, y, m2: jnp.mean((m2(m1(x)) - y) ** 2)
-    grad_fn = nnx.grad(loss_fn, argnums=(1, 3), wrt=nnx.Param)
+    grad_fn = nnx.grad(loss_fn, argnums=argnums, wrt=nnx.Param)
     x = jax.random.uniform(rngs(), (1, 2))
     y = jnp.ones((1, 3))
-    grads_m1, grads_m2 = grad_fn(x, m1, y, m2)
+    inputs = [x, y]
+    inputs.insert(argnums[0], m1)
+    inputs.insert(argnums[1], m2)
+    grads_m1, grads_m2 = grad_fn(*inputs)
 
     assert 'kernel' in grads_m1
     assert grads_m1.kernel.value.shape == (2, 3)