diff --git a/src/lerobot/policies/pretrained.py b/src/lerobot/policies/pretrained.py
index 3f5d89ec5..a1499d077 100644
--- a/src/lerobot/policies/pretrained.py
+++ b/src/lerobot/policies/pretrained.py
@@ -206,6 +206,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
     def push_model_to_hub(
         self,
         cfg: TrainPipelineConfig,
+        peft_model=None,
     ):
         api = HfApi()
         repo_id = api.create_repo(
@@ -216,7 +217,14 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         with TemporaryDirectory(ignore_cleanup_errors=True) as tmp:
             saved_path = Path(tmp) / repo_id
 
-            self.save_pretrained(saved_path)  # Calls _save_pretrained and stores model tensors
+            if peft_model is not None:
+                # Since PEFT just forwards calls to `push_model_to_hub`, `self` is not the PeftModel wrapper
+                # but the actual policy which is why we need the PEFT model passed to us to save the adapter.
+                # That also means that we need to store the policy config ourselves since PEFT can't.
+                peft_model.save_pretrained(saved_path)
+                self.config.save_pretrained(saved_path)
+            else:
+                self.save_pretrained(saved_path)  # Calls _save_pretrained and stores model tensors
 
             card = self.generate_model_card(
                 cfg.dataset.repo_id, self.config.type, self.config.license, self.config.tags
diff --git a/src/lerobot/scripts/lerobot_train.py b/src/lerobot/scripts/lerobot_train.py
index 9c85e9dc0..d8822a670 100644
--- a/src/lerobot/scripts/lerobot_train.py
+++ b/src/lerobot/scripts/lerobot_train.py
@@ -524,7 +524,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
 
         if cfg.policy.push_to_hub:
             unwrapped_policy = accelerator.unwrap_model(policy)
-            unwrapped_policy.push_model_to_hub(cfg)
+            if cfg.policy.use_peft:
+                unwrapped_policy.push_model_to_hub(cfg, peft_model=unwrapped_policy)
+            else:
+                unwrapped_policy.push_model_to_hub(cfg)
             preprocessor.push_to_hub(cfg.policy.repo_id)
             postprocessor.push_to_hub(cfg.policy.repo_id)
 
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 2f5ddebe8..3760e4b78 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -1,5 +1,6 @@
 import importlib
-from unittest.mock import patch
+import os
+from unittest.mock import MagicMock, patch
 
 import pytest
 from safetensors.torch import load_file
@@ -19,6 +20,40 @@ def lerobot_record(args):
     return run_command(cmd="lerobot-record", module="lerobot_record", args=args)
 
 
+@pytest.mark.parametrize("policy_type", ["smolvla"])
+def test_peft_training_push_to_hub_works(policy_type, tmp_path):
+    """Ensure that push to hub stores PEFT only the adapter, not the full model weights."""
+    output_dir = tmp_path / f"output_{policy_type}"
+    upload_folder_contents = set()
+
+    def mock_upload_folder(*args, **kwargs):
+        folder_path = kwargs['folder_path']
+        # we include more than is actually uploaded since we ignore {allow,ignore}_patterns of upload_folders()
+        upload_folder_contents.update(os.listdir(folder_path))
+        return MagicMock()
+
+    with (
+        patch("huggingface_hub.HfApi.create_repo"),
+        patch("huggingface_hub.HfApi.upload_folder", mock_upload_folder),
+    ):
+        lerobot_train(
+            [
+                f"--policy.type={policy_type}",
+                "--policy.push_to_hub=true",
+                "--policy.repo_id=foo/bar",
+                "--peft.method=LORA",
+                "--dataset.repo_id=lerobot/pusht",
+                "--dataset.episodes=[0, 1]",
+                "--steps=1",
+                f"--output_dir={output_dir}",
+            ]
+        )
+
+        assert 'adapter_model.safetensors' in upload_folder_contents
+        assert 'config.json' in upload_folder_contents
+        assert 'adapter_config.json' in upload_folder_contents
+
+
 @pytest.mark.parametrize("policy_type", ["smolvla"])
 def test_peft_training_works(policy_type, tmp_path):
     """Check whether the standard case of fine-tuning a (partially) pre-trained policy with PEFT works."""