Disallow PEFT training on non-pretrained policies

At first I thought it would make sense to have this feature
in case you want to fine-tune a pre-trained section but in the
end it makes more trouble than it's worth.

It's still possible to allow this in the future when a concrete
need arises.

src/lerobot/scripts/lerobot_train.py

@@ -153,9 +153,9 @@ def wrap_policy_in_peft_model(cfg, policy):
         p.requires_grad_(False)
 
     if not cfg.policy.pretrained_path:
-        logging.warning(
+        raise ValueError(
             "Training from scratch using PEFT. This is unlikely to yield good results. "
-            "Consider supplying a `policy.path` to fine-tune an existing model."
+            "Supply a `policy.path` to fine-tune an existing model."
         )
 
     if cfg.policy.type == "smolvla" and not cfg.policy.load_vlm_weights:
@@ -195,7 +195,8 @@ def wrap_policy_in_peft_model(cfg, policy):
     # PEFT uses this attribute to set adapter_config.base_name_or_path which we use for loading the
     # correct base model in `make_policy` since in a PEFT loading setting we only get the path to the
     # adapter, not the base model.
-    policy.name_or_path = str(policy.config.pretrained_path)
+    if policy.config.pretrained_path:
+        policy.name_or_path = str(policy.config.pretrained_path)
 
     # Finally wrap the policy in a PEFT model
     policy = get_peft_model(

tests/test_cli.py

@@ -20,12 +20,22 @@ def lerobot_record(args):
     return run_command(cmd="lerobot-record", module="lerobot_record", args=args)
 
 
+def resolve_model_id_for_peft_training(policy_type):
+    """PEFT training needs pretrained models, this finds the pretrained model of a policy type for PEFT training."""
+    if policy_type == "smolvla":
+        return "lerobot/smolvla_base"
+
+    raise ValueError(f"No pretrained model known for {policy_type}. PEFT training will not work.")
+
+
 @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()
 
+    model_id = resolve_model_id_for_peft_training(policy_type)
+
     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()
@@ -38,9 +48,11 @@ def test_peft_training_push_to_hub_works(policy_type, tmp_path):
     ):
         lerobot_train(
             [
-                f"--policy.type={policy_type}",
+                f"--policy.path={model_id}",
                 "--policy.push_to_hub=true",
                 "--policy.repo_id=foo/bar",
+                "--policy.input_features=null",
+                "--policy.output_features=null",
                 "--peft.method=LORA",
                 "--dataset.repo_id=lerobot/pusht",
                 "--dataset.episodes=[0, 1]",
@@ -58,11 +70,14 @@ def test_peft_training_push_to_hub_works(policy_type, tmp_path):
 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."""
     output_dir = tmp_path / f"output_{policy_type}"
+    model_id = resolve_model_id_for_peft_training(policy_type)
 
     lerobot_train(
         [
-            f"--policy.type={policy_type}",
+            f"--policy.path={model_id}",
             "--policy.push_to_hub=false",
+            "--policy.input_features=null",
+            "--policy.output_features=null",
             "--peft.method=LORA",
             "--dataset.repo_id=lerobot/pusht",
             "--dataset.episodes=[0, 1]",
@@ -81,7 +96,7 @@ def test_peft_training_works(policy_type, tmp_path):
     # so these must be part of the trained state dict.
     state_dict = load_file(policy_dir / "adapter_model.safetensors")
 
-    fully_trained_keys = [
+    adapted_keys = [
         "state_proj",
         "action_in_proj",
         "action_out_proj",
@@ -91,18 +106,19 @@ def test_peft_training_works(policy_type, tmp_path):
 
     found_keys = [
         module_key
-        for module_key in fully_trained_keys
+        for module_key in adapted_keys
         for state_dict_key in state_dict
         if f".{module_key}." in state_dict_key
     ]
 
-    assert set(found_keys) == set(fully_trained_keys)
+    assert set(found_keys) == set(adapted_keys)
 
 
 @pytest.mark.parametrize("policy_type", ["smolvla"])
 def test_peft_training_params_are_fewer(policy_type, tmp_path):
     """Check whether the standard case of fine-tuning a (partially) pre-trained policy with PEFT works."""
     output_dir = tmp_path / f"output_{policy_type}"
+    model_id = resolve_model_id_for_peft_training(policy_type)
 
     def dummy_update_policy(
         train_metrics,
@@ -123,8 +139,10 @@ def test_peft_training_params_are_fewer(policy_type, tmp_path):
     with patch("lerobot.scripts.lerobot_train.update_policy", dummy_update_policy):
         lerobot_train(
             [
-                f"--policy.type={policy_type}",
+                f"--policy.path={model_id}",
                 "--policy.push_to_hub=false",
+                "--policy.input_features=null",
+                "--policy.output_features=null",
                 "--peft.method=LORA",
                 "--dataset.repo_id=lerobot/pusht",
                 "--dataset.episodes=[0, 1]",
@@ -139,6 +157,7 @@ class DummyRobot:
     cameras = []
     action_features = {"foo": 1.0, "bar": 2.0}
     observation_features = {"obs1": 1.0, "obs2": 2.0}
+    is_connected = True
 
     def connect(self, *args):
         pass
@@ -157,11 +176,14 @@ def test_peft_record_loads_policy(policy_type, tmp_path):
     from peft import PeftModel
 
     output_dir = tmp_path / f"output_{policy_type}"
+    model_id = resolve_model_id_for_peft_training(policy_type)
 
     lerobot_train(
         [
-            f"--policy.type={policy_type}",
+            f"--policy.path={model_id}",
             "--policy.push_to_hub=false",
+            "--policy.input_features=null",
+            "--policy.output_features=null",
             "--peft.method=LORA",
             "--dataset.repo_id=lerobot/pusht",
             "--dataset.episodes=[0, 1]",