diff --git a/torchft/local_sgd_integ_test.py b/torchft/local_sgd_integ_test.py
index 355e4877..2b0e065e 100644
--- a/torchft/local_sgd_integ_test.py
+++ b/torchft/local_sgd_integ_test.py
@@ -9,7 +9,7 @@
 from contextlib import ExitStack
 from dataclasses import field
 from datetime import timedelta
-from typing import Any, Dict
+from typing import Any, Dict, cast
 from unittest import TestCase, skipIf
 
 import torch
@@ -157,17 +157,24 @@ def diloco_train_loop(
         inner_optimizer: optim.Optimizer = torch.optim.AdamW(
             m.parameters(), lr=4e-4, weight_decay=0.1, betas=(0.9, 0.95)
         )
-        outer_optimizer: optim.Optimizer = torch.optim.SGD(
-            m.parameters(), lr=0.7, momentum=0.9, nesterov=True
-        )
+
+        # Create one outer optimizer per fragment
+        outer_optimizers = []
+        for _, layer in enumerate(m.layers):
+            outer_optimizers.append(
+                torch.optim.SGD(layer.parameters(), lr=0.7, momentum=0.9, nesterov=True)
+            )
 
         # pyre-ignore[53]
         def load_state_dict(state_dict: Dict[str, Dict[str, object]]) -> None:
             m.load_state_dict(state_dict["model"])
             m.to(device)
 
+            # Load original parameters for each fragment
             for i, fragment in enumerate(diloco._fragments):
-                fragment.original_parameters = state_dict["original_params"][f"{i}"]
+                fragment.original_parameters = cast(
+                    Dict[str, torch.Tensor], state_dict["original_params"][f"{i}"]
+                )
 
             for fragment in diloco._fragments:
                 for name in fragment.original_parameters.keys():
@@ -176,7 +183,8 @@ def load_state_dict(state_dict: Dict[str, Dict[str, object]]) -> None:
                     ].to(device)
 
             inner_optimizer.load_state_dict(state_dict["inner_optim"])
-            outer_optimizer.load_state_dict(state_dict["outer_optim"])
+            for i, optimizer in enumerate(outer_optimizers):
+                optimizer.load_state_dict(state_dict[f"outer_optim"][f"{i}"])
 
         def state_dict() -> Dict[str, Dict[str, object]]:  # pyre-ignore[53]
             return {
@@ -186,7 +194,10 @@ def state_dict() -> Dict[str, Dict[str, object]]:  # pyre-ignore[53]
                     for i, fragment in enumerate(diloco._fragments)
                 },
                 "inner_optim": inner_optimizer.state_dict(),
-                "outer_optim": outer_optimizer.state_dict(),
+                "outer_optim": {
+                    f"{i}": optimizer.state_dict()
+                    for i, optimizer in enumerate(outer_optimizers)
+                },
             }
 
         print(f"worker {runner.replica_id=} {rank=} {runner.world_size=} starting")
@@ -259,7 +270,7 @@ def state_dict() -> Dict[str, Dict[str, object]]:  # pyre-ignore[53]
             manager,
             [layer for layer in m.layers],
             inner_optimizer,
-            outer_optimizer,
+            outer_optimizers,
             backup_device=device,
             **diloco_args,
         ) as diloco:
@@ -305,11 +316,26 @@ def assert_equal_global_state(
             rep0[step]["user"]["default"]["original_params"],
             check_device=False,
         )
-        torch.testing.assert_close(
-            rep1[step]["user"]["default"]["outer_optim"],
-            rep0[step]["user"]["default"]["outer_optim"],
-            check_device=False,
-        )
+        # Check all outer optimizers
+        for i in range(
+            len(
+                cast(
+                    dict[str, dict[str, torch.Tensor]],
+                    rep0[step]["user"]["default"]["outer_optim"],
+                ).keys()
+            )
+        ):
+            torch.testing.assert_close(
+                cast(
+                    dict[str, dict[str, torch.Tensor]],
+                    rep1[step]["user"]["default"]["outer_optim"],
+                )[f"{i}"],
+                cast(
+                    dict[str, dict[str, torch.Tensor]],
+                    rep0[step]["user"]["default"]["outer_optim"],
+                )[f"{i}"],
+                check_device=False,
+            )
 
 
 class LocalSGDIntegTest(TestCase):
@@ -420,18 +446,7 @@ def test_diloco_healthy(self, use_cuda: bool) -> None:
         lighthouse.shutdown()
 
         rep0, rep1 = state_dicts
-        for step, state_dict in rep1.items():
-            # inner optimizer will be different, outer optimizer and model should be the same
-            torch.testing.assert_close(
-                state_dict["user"]["default"]["model"],
-                rep0[step]["user"]["default"]["model"],
-                check_device=False,
-            )
-            torch.testing.assert_close(
-                state_dict["user"]["default"]["outer_optim"],
-                rep0[step]["user"]["default"]["outer_optim"],
-                check_device=False,
-            )
+        assert_equal_global_state(rep1, rep0)
 
     # pyre-fixme[56]: Pyre was not able to infer the type of argument
     @skipIf(sys.platform == "darwin", "not reliable on mac")