diff --git a/torchtitan/components/optimizer.py b/torchtitan/components/optimizer.py
index f45d4db6ce..6a84cddcde 100644
--- a/torchtitan/components/optimizer.py
+++ b/torchtitan/components/optimizer.py
@@ -180,6 +180,7 @@ def __init__(
         optimizer_cls: type[T],
         optimizer_kwargs: dict[str, Any],
         ft_manager: "ft.Manager",
+        use_ft_optimizer: bool = True,
     ) -> None:
         super().__init__(model_parts, optimizer_cls, optimizer_kwargs)
 
@@ -192,7 +193,9 @@ def __init__(
         }
         self.cache_state_dict: dict[str, Any] = {}
         self._ft_optimizer = ft.Optimizer(ft_manager, self)
-        self._call_from_ft: bool = False
+        # Whether to determine quorum using FT.optimizer,
+        # in semi-sync training we use the synchronization step to start quorum
+        self._use_ft_optimizer: bool = use_ft_optimizer
 
     def init_cache_state_dict(self) -> None:
         self.cache_state_dict = super().state_dict()
@@ -211,28 +214,28 @@ def load_state_dict(self, state_dict: dict[str, Any]) -> None:
     def step(self, *args, **kwargs) -> None:
         """Calling the correct step() depending on the caller.
 
-        TorchFT's OptimizerWrapper.step() is designed to be callled only once
+        TorchFT's OptimizerWrapper.step() is designed to be called only once
         per train step per ft.Manager regardless how many optimizers are used.
         Hence we will need to appropriately dispatch the call.
         """
-        if self._call_from_ft:
-            super().step(*args, **kwargs)
-        else:
-            self._call_from_ft = True
+        if self._use_ft_optimizer:
+            self._use_ft_optimizer = False
             self._ft_optimizer.step(*args, **kwargs)
-            self._call_from_ft = False
+            self._use_ft_optimizer = True
+        else:
+            super().step(*args, **kwargs)
 
     def zero_grad(self, *args, **kwargs) -> None:
         """Calling the correct zero_grad() depending on the caller.
 
         Check the comment in ``step()``.
         """
-        if self._call_from_ft:
-            super().zero_grad(*args, **kwargs)
-        else:
-            self._call_from_ft = True
+        if self._use_ft_optimizer:
+            self._use_ft_optimizer = False
             self._ft_optimizer.zero_grad(*args, **kwargs)
-            self._call_from_ft = False
+            self._use_ft_optimizer = True
+        else:
+            super().zero_grad(*args, **kwargs)
 
 
 def build_optimizers(
@@ -297,7 +300,11 @@ def build_optimizers(
         )
     elif ft_manager.enabled:
         return FTOptimizersContainer(
-            model_parts, optimizer_cls, optimizer_kwargs, ft_manager.manager
+            model_parts,
+            optimizer_cls,
+            optimizer_kwargs,
+            ft_manager.manager,
+            use_ft_optimizer=job_config.fault_tolerance.semi_sync_method is None,
         )
     else:
         return OptimizersContainer(model_parts, optimizer_cls, optimizer_kwargs)
diff --git a/torchtitan/train.py b/torchtitan/train.py
index d0228ae782..88340fba69 100644
--- a/torchtitan/train.py
+++ b/torchtitan/train.py
@@ -230,7 +230,10 @@ def __init__(self, job_config: JobConfig):
 
             self.model_parts = [model]
 
-        if self.ft_manager.enabled:
+        if (
+            self.ft_manager.enabled
+            and job_config.fault_tolerance.semi_sync_method is None
+        ):
             self.ft_manager.set_all_reduce_hook(self.model_parts)
 
         # initialize device memory monitor and get peak flops for MFU calculation
@@ -388,7 +391,12 @@ def train_step(self, input_dict: dict[str, torch.Tensor], labels: torch.Tensor):
             or self.ft_manager.enabled
         ):
             loss = loss.detach()
-            ft_pg = self.ft_manager.replicate_pg if self.ft_manager.enabled else None
+            # Skip ft manager communication when using semi sync training
+            use_ft_pg = (
+                self.ft_manager.enabled
+                and self.job_config.fault_tolerance.semi_sync_method is None
+            )
+            ft_pg = self.ft_manager.replicate_pg if use_ft_pg else None
             global_avg_loss, global_max_loss = (
                 dist_utils.dist_mean(loss, world_mesh["dp_cp"], ft_pg),
                 dist_utils.dist_max(loss, world_mesh["dp_cp"], ft_pg),