diff --git a/torchrec/distributed/train_pipeline/tests/test_train_pipelines.py b/torchrec/distributed/train_pipeline/tests/test_train_pipelines.py
index 8ff170992..0f734281f 100644
--- a/torchrec/distributed/train_pipeline/tests/test_train_pipelines.py
+++ b/torchrec/distributed/train_pipeline/tests/test_train_pipelines.py
@@ -7,6 +7,7 @@
 
 # pyre-strict
 
+import contextlib
 import copy
 
 import unittest
@@ -862,7 +863,7 @@ def custom_model_fwd(
             batch_size = pred.size(0)
             return loss, pred.expand(batch_size * 2, -1)
 
-        pipeline = TrainPipelineSparseDist(
+        pipeline = self.pipeline_class(
             model=sharded_model_pipelined,
             optimizer=optim_pipelined,
             device=self.device,
@@ -2201,17 +2202,26 @@ def gpu_preproc(x: StageOut) -> StageOut:
 
 
 class TrainPipelineSparseDistCompAutogradTest(TrainPipelineSparseDistTest):
+    _exit_stack: contextlib.ExitStack
+
     def setUp(self) -> None:
         super().setUp()
+        torch.manual_seed(42)
         self.pipeline_class = TrainPipelineSparseDistCompAutograd
         torch._dynamo.reset()
         counters["compiled_autograd"].clear()
         # Compiled Autograd don't work with Anomaly Mode
         torch.autograd.set_detect_anomaly(False)
+        self._exit_stack = contextlib.ExitStack()
+        self._exit_stack.enter_context(
+            torch._dynamo.config.patch(
+                optimize_ddp="python_reducer_without_compiled_forward"
+            ),
+        )
 
     def tearDown(self) -> None:
-        # Every single test has two captures, one for forward and one for backward
-        self.assertEqual(counters["compiled_autograd"]["captures"], 2)
+        self._exit_stack.close()
+        self.assertEqual(counters["compiled_autograd"]["captures"], 3)
         return super().tearDown()
 
     @unittest.skip("Dynamo only supports FSDP with use_orig_params=True")
@@ -2219,3 +2229,14 @@ def tearDown(self) -> None:
     @given(execute_all_batches=st.booleans())
     def test_pipelining_fsdp_pre_trace(self, execute_all_batches: bool) -> None:
         super().test_pipelining_fsdp_pre_trace()
+
+    @unittest.skip(
+        "TrainPipelineSparseDistTest.test_equal_to_non_pipelined was called from multiple different executors, which fails hypothesis HealthChek, so we skip it here"
+    )
+    def test_equal_to_non_pipelined(
+        self,
+        sharding_type: str,
+        kernel_type: str,
+        execute_all_batches: bool,
+    ) -> None:
+        super().test_equal_to_non_pipelined()