diff --git a/lerobot/configs/policies.py b/lerobot/configs/policies.py
index a85ca187f..c4e344e7e 100644
--- a/lerobot/configs/policies.py
+++ b/lerobot/configs/policies.py
@@ -60,6 +60,9 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
     # automatic gradient scaling is used.
     use_amp: bool = False
 
+    # Whether the policy employed PEFT for training.
+    use_peft: bool = False
+
     def __post_init__(self):
         self.pretrained_path = None
         if not self.device or not is_torch_device_available(self.device):
diff --git a/lerobot/record.py b/lerobot/record.py
index 8ae34d76c..4f3c32c3a 100644
--- a/lerobot/record.py
+++ b/lerobot/record.py
@@ -130,6 +130,21 @@ class DatasetRecordConfig:
             raise ValueError("You need to provide a task as argument in `single_task`.")
 
 
+def get_policy_config_from_peft_checkpoint(peft_config):
+    if getattr(peft_config, "auto_mapping", None) is None:
+        raise ValueError(
+            "No auto-mapping config found in adapter config. Cannot determine policy config."
+        )
+
+    auto_mapping = getattr(peft_config, "auto_mapping", None)
+    base_model_class = auto_mapping["base_model_class"]
+    parent_library_name = auto_mapping["parent_library"]
+
+    parent_library = importlib.import_module(parent_library_name)
+    target_class = getattr(parent_library, base_model_class)
+    return target_class.config_class
+
+
 @dataclass
 class RecordConfig:
     robot: RobotConfig
@@ -152,28 +167,18 @@ class RecordConfig:
         if policy_path:
             cli_overrides = parser.get_cli_overrides("policy")
 
-            if (policy_path / 'adapter_config.json').exists():
+            if (Path(policy_path) / 'adapter_config.json').exists():
                 # The pretrained checkpoint is a PEFT adapter, cool. Currently we don't upload the
                 # policy's config alongside the adapter config but to initialize the policy we
                 # need a policy config. We assume that the config hasn't changed and we infer
                 # the policy's config class from the base class mentioned in the adapter config.
                 self.peft_config = PeftConfig.from_pretrained(policy_path)
 
-                if getattr(self.peft_config, "auto_mapping", None) is None:
-                    raise ValueError(
-                        "No auto-mapping config found in adapter config. Cannot determine policy config."
-                    )
-
-                auto_mapping = getattr(self.peft_config, "auto_mapping", None)
-                base_model_class = auto_mapping["base_model_class"]
-                parent_library_name = auto_mapping["parent_library"]
-
-                parent_library = importlib.import_module(parent_library_name)
-                target_class = getattr(parent_library, base_model_class)
-                policy_config_class = target_class.config_class
+                policy_config_class = get_policy_config_from_peft_checkpoint(self.peft_config)
 
                 self.policy = policy_config_class()
                 self.policy.pretrained_path = policy_path
+                self.policy.use_peft = True
 
             else:
                 self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
@@ -308,7 +313,7 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
 
     # Load pretrained policy
 
-    if cfg.use_peft:
+    if cfg.policy.use_peft:
         # in case of PEFT we re-use the policy pretrained path to point to the adapter path.
         peft_path = cfg.policy.pretrained_path
         cfg.policy.pretrained_path = None