diff --git a/tests/pallas/pallas_jumble_test.py b/tests/pallas/pallas_jumble_test.py
index 8452d1ee7264..509ef08a987f 100644
--- a/tests/pallas/pallas_jumble_test.py
+++ b/tests/pallas/pallas_jumble_test.py
@@ -41,6 +41,21 @@
 floatx = dtypes.canonicalize_dtype(jnp.float64)
 
 
+def _assert_ragged_equal_with_elementwise_mask(
+    row_count, col_grid_size, ragged_shape, res, ref
+):
+  total_columns = col_grid_size * 128
+  mask = jnp.zeros((len(ragged_shape), row_count, total_columns), dtype=bool)
+
+  for i, r in enumerate(ragged_shape):
+    mask = mask.at[i, :, : r * 128].set(True)
+
+  res_valid = jnp.where(mask, res, -1)
+  ref_valid = jnp.where(mask, ref, -1)
+
+  np.testing.assert_allclose(res_valid, ref_valid)
+
+
 @jtu.with_config(jax_traceback_filtering="off")
 class PallasBaseTest(jtu.JaxTestCase):
   INTERPRET = False
@@ -104,24 +119,16 @@ def invoke_kernel(x):
         axis_size=3,
     )(x)
 
-    res = res.data
-    total = len(ragged_shape) * row_count * col_grid_size * 128
-    res_total = np.prod(res.shape)
-    self.assertEqual(res_total, total)
-    ragged_total = 0
-    for dim in ragged_shape:
-      ragged_total += row_count * dim * 128
-
-    def correct(v):
-      return np.count_nonzero(v == jnp.sin(1.0))
-
-    for b, batch in enumerate(res):
-      ragged_val = ragged_shape[b]
-      for r, row in enumerate(batch):
-        row_total = ragged_val * 128
-        self.assertEqual(correct(row), row_total, msg=f"row {r}, : {row}")
+    ref = jax.vmap(
+        jnp.sin,
+        out_axes=batching.jumble_axis,
+        in_axes=batching.jumble_axis,
+        axis_size=3,
+    )(x)
 
-    self.assertEqual(correct(res), ragged_total)
+    _assert_ragged_equal_with_elementwise_mask(
+        row_count, col_grid_size, ragged_shape, res.data, ref.data
+    )
 
   def test_vmap_jumble_over_add_kernel(self):
     if not jtu.test_device_matches(["tpu"]):
@@ -156,36 +163,34 @@ def invoke_kernel(x, y):
               (8, col_grid_size * 128), dtype=jnp.float32
           ),
           grid=(1, col_grid_size),
-          interpret=False,
+          interpret=self.INTERPRET,
       )(x, y)
 
-    res = jax.vmap(
-        invoke_kernel,
-        out_axes=batching.jumble_axis,
-        in_axes=batching.jumble_axis,
-        axis_size=3,
-    )(x, y)
+    # We've had this test fail with data corruption due to multiple
+    # invocations, so we run it k times to make sure it's not setting up
+    # memory incorrectly for subsequent invocations.
+    for _ in range(4):
+      res = jax.vmap(
+          invoke_kernel,
+          out_axes=batching.jumble_axis,
+          in_axes=batching.jumble_axis,
+          axis_size=3,
+      )(x, y)
 
-    res = res.data
-    total = len(ragged_shape) * row_count * col_grid_size * 128
-    res_total = np.prod(res.shape)
-    self.assertEqual(res_total, total)
-    ragged_total = 0
-    for dim in ragged_shape:
-      ragged_total += row_count * dim * 128
-
-    def correct(v):
-      return np.count_nonzero(v == 2.0)
-
-    for r, row in enumerate(res):
-      ragged_val = ragged_shape[r]
-      row_total = ragged_val * 128 * row_count
-      self.assertEqual(correct(row), row_total)
-      for col in row:
-        col_total = ragged_val * 128
-        self.assertEqual(correct(col), col_total)
-
-    self.assertEqual(np.count_nonzero(res == 2.0), ragged_total)
+      res = res.data
+      total = len(ragged_shape) * row_count * col_grid_size * 128
+      res_total = np.prod(res.shape)
+      self.assertEqual(res_total, total)
+
+      ref = jax.vmap(
+          lambda x, y: x + y,
+          out_axes=batching.jumble_axis,
+          in_axes=batching.jumble_axis,
+          axis_size=3,
+      )(x, y)
+      _assert_ragged_equal_with_elementwise_mask(
+          row_count, col_grid_size, ragged_shape, res, ref.data
+      )
 
   def test_vmap_jumble_over_sin_kernel_grid_remapping(self):
     if not jtu.test_device_matches(["tpu"]):
@@ -212,7 +217,7 @@ def invoke_kernel(x):
           out_specs=pl.BlockSpec((8, 128), lambda j, k: (j, k)),
           out_shape=jax.ShapeDtypeStruct((8, 640), dtype=jnp.float32),
           grid=(1, 5),
-          interpret=False,
+          interpret=self.INTERPRET,
       )(x)
 
     with self.assertRaisesRegex(ValueError, "Axis 2 is out of bounds for grid"):
@@ -280,7 +285,7 @@ def matmul(
           ),
           grid=grid,
           input_output_aliases={2: 0},
-          interpret=False,
+          interpret=self.INTERPRET,
       )(x, y, x_sentinel)
 
     # TODO(mvoz): parameterize this shape?