diff --git a/monai/networks/nets/fullyconnectednet.py b/monai/networks/nets/fullyconnectednet.py
index be179e5b59..ace07e9cc3 100644
--- a/monai/networks/nets/fullyconnectednet.py
+++ b/monai/networks/nets/fullyconnectednet.py
@@ -172,12 +172,11 @@ def decode_forward(self, z: torch.Tensor, use_sigmoid: bool = True) -> torch.Ten
         return x
 
     def reparameterize(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:
-        std = torch.exp(0.5 * logvar)
+        if self.training:  # reparameterization trick only during training
+            std = torch.exp(0.5 * logvar)
+            return mu + torch.randn_like(std) * std
 
-        if self.training:  # multiply random noise with std only during training
-            std = torch.randn_like(std).mul(std)
-
-        return std.add_(mu)
+        return mu
 
     def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         mu, logvar = self.encode_forward(x)
diff --git a/monai/networks/nets/varautoencoder.py b/monai/networks/nets/varautoencoder.py
index 0674094aa7..28fbded86e 100644
--- a/monai/networks/nets/varautoencoder.py
+++ b/monai/networks/nets/varautoencoder.py
@@ -142,12 +142,11 @@ def decode_forward(self, z: torch.Tensor, use_sigmoid: bool = True) -> torch.Ten
         return x
 
     def reparameterize(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:
-        std = torch.exp(0.5 * logvar)
+        if self.training:  # reparameterization trick only during training
+            std = torch.exp(0.5 * logvar)
+            return mu + torch.randn_like(std) * std
 
-        if self.training:  # multiply random noise with std only during training
-            std = torch.randn_like(std).mul(std)
-
-        return std.add_(mu)
+        return mu
 
     def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         mu, logvar = self.encode_forward(x)