diff --git a/vista3d/scripts/train.py b/vista3d/scripts/train.py
index e9beb8e..e3a1332 100644
--- a/vista3d/scripts/train.py
+++ b/vista3d/scripts/train.py
@@ -216,10 +216,6 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
     optimizer = optimizer_part.instantiate(params=model.parameters())
     lr_scheduler_part = parser.get_parsed_content("lr_scheduler", instantiate=False)
     lr_scheduler = lr_scheduler_part.instantiate(optimizer=optimizer)
-    if world_size > 1:
-        model = DistributedDataParallel(
-            model, device_ids=[device], find_unused_parameters=True
-        )
     if finetune["activate"] and os.path.isfile(finetune["pretrained_ckpt_name"]):
         logger.debug(
             "Fine-tuning pre-trained checkpoint {:s}".format(
@@ -229,13 +225,14 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
         pretrained_ckpt = torch.load(
             finetune["pretrained_ckpt_name"], map_location=device
         )
-        copy_model_state(
-            model, pretrained_ckpt, exclude_vars=finetune.get("exclude_vars")
-        )
+        model.load_state_dict(pretrained_ckpt)
         del pretrained_ckpt
     else:
         logger.debug("Training from scratch")
-
+    if world_size > 1:
+        model = DistributedDataParallel(
+            model, device_ids=[device], find_unused_parameters=True
+        )
     # training hyperparameters - sample
     num_images_per_batch = parser.get_parsed_content("num_images_per_batch")
     num_patches_per_iter = parser.get_parsed_content("num_patches_per_iter")
diff --git a/vista3d/scripts/train_finetune.py b/vista3d/scripts/train_finetune.py
index bb945ee..9a59735 100644
--- a/vista3d/scripts/train_finetune.py
+++ b/vista3d/scripts/train_finetune.py
@@ -149,10 +149,6 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
     optimizer = optimizer_part.instantiate(params=model.parameters())
     lr_scheduler_part = parser.get_parsed_content("lr_scheduler", instantiate=False)
     lr_scheduler = lr_scheduler_part.instantiate(optimizer=optimizer)
-    if world_size > 1:
-        model = DistributedDataParallel(
-            model, device_ids=[device], find_unused_parameters=True
-        )
     if finetune["activate"] and os.path.isfile(finetune["pretrained_ckpt_name"]):
         logger.debug(
             "Fine-tuning pre-trained checkpoint {:s}".format(
@@ -162,13 +158,15 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
         pretrained_ckpt = torch.load(
             finetune["pretrained_ckpt_name"], map_location=device
         )
-        copy_model_state(
-            model, pretrained_ckpt, exclude_vars=finetune.get("exclude_vars")
-        )
+        model.load_state_dict(pretrained_ckpt)
         del pretrained_ckpt
     else:
         logger.debug("Training from scratch")
 
+    if world_size > 1:
+        model = DistributedDataParallel(
+            model, device_ids=[device], find_unused_parameters=True
+        )
     # training hyperparameters - sample
     num_images_per_batch = parser.get_parsed_content("num_images_per_batch")
     num_patches_per_iter = parser.get_parsed_content("num_patches_per_iter")