diff --git a/experiments/robot/openvla_utils.py b/experiments/robot/openvla_utils.py
new file mode 100644
index 0000000..ec9d757
--- /dev/null
+++ b/experiments/robot/openvla_utils.py
@@ -0,0 +1,48 @@
+"""Utils for evaluating OpenVLA or fine-tuned OpenVLA policies."""
+
+import json
+import os
+
+import torch
+from huggingface_hub import HfApi, hf_hub_download
+
+
+def model_is_on_hf_hub(model_path: str) -> bool:
+    """Checks whether a model path points to a model on Hugging Face Hub."""
+    # If the API call below runs without error, the model is on the hub
+    try:
+        HfApi().model_info(model_path)
+        return True
+    except Exception:
+        return False
+
+def _load_dataset_stats(vla: torch.nn.Module, checkpoint_path: str) -> None:
+    """
+    Load dataset statistics used during training for action normalization.
+
+    Args:
+        vla: The VLA model
+        checkpoint_path: Path to the checkpoint directory
+    """
+    if model_is_on_hf_hub(checkpoint_path):
+        # Download dataset stats directly from HF Hub
+        try:
+            dataset_statistics_path = hf_hub_download(
+                repo_id=checkpoint_path,
+                filename="dataset_statistics.json",
+            )
+        except Exception as e:
+            print(f"Failed to download dataset_statistics.json from HF Hub: {e}")
+            dataset_statistics_path = ""
+    else:
+        dataset_statistics_path = os.path.join(checkpoint_path, "dataset_statistics.json")
+    if os.path.isfile(dataset_statistics_path):
+        with open(dataset_statistics_path, "r") as f:
+            norm_stats = json.load(f)
+        vla.norm_stats = norm_stats
+    else:
+        print(
+            "WARNING: No local dataset_statistics.json file found for current checkpoint.\n"
+            "You can ignore this if you are loading the base VLA (i.e. not fine-tuned) checkpoint."
+            "Otherwise, you may run into errors when trying to call `predict_action()` due to an absent `unnorm_key`."
+        )
diff --git a/verl/workers/fsdp_workers.py b/verl/workers/fsdp_workers.py
index 7463b96..9e4c853 100644
--- a/verl/workers/fsdp_workers.py
+++ b/verl/workers/fsdp_workers.py
@@ -45,6 +45,8 @@
 from peft import LoraConfig, PeftModel, get_peft_model, TaskType
 import json
 
+from experiments.robot.openvla_utils import _load_dataset_stats
+
 
 logger = logging.getLogger(__file__)
 logger.setLevel(os.getenv('VERL_PPO_LOGGING_LEVEL', 'WARN'))
@@ -196,17 +198,7 @@ def _build_model_optimizer(self,
                 #oft add
                 actor_module.vision_backbone.set_num_images_in_input(self.config.actor.num_images_in_input)
                 
-                dataset_statistics_path = os.path.join(local_path, "dataset_statistics.json")
-                if os.path.isfile(dataset_statistics_path):
-                    with open(dataset_statistics_path, "r") as f:
-                        norm_stats = json.load(f)
-                    actor_module.norm_stats = norm_stats
-                else:
-                    print(
-                        "WARNING: No local dataset_statistics.json file found for current checkpoint.\n"
-                        "You can ignore this if you are loading the base VLA (i.e. not fine-tuned) checkpoint."
-                        "Otherwise, you may run into errors when trying to call `predict_action()` due to an absent `unnorm_key`."
-                    )
+                _load_dataset_stats(actor_module, local_path)
             elif self.config.model.vla == "openvla":
                 actor_module = AutoModelForVision2Seq.from_pretrained(
                                                     pretrained_model_name_or_path=local_path,