diff --git a/lerobot/common/utils/train_utils.py b/lerobot/common/utils/train_utils.py
index a79983128..6848e7db6 100644
--- a/lerobot/common/utils/train_utils.py
+++ b/lerobot/common/utils/train_utils.py
@@ -99,6 +99,10 @@ def save_checkpoint(
     pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR
     policy.save_pretrained(pretrained_dir)
     cfg.save_pretrained(pretrained_dir)
+    if cfg.use_peft:
+        # When using PEFT, policy.save_pretrained will only write the adapter weights + config, not the
+        # policy config which we need for loading the model. In this case we'll write it ourselves.
+        policy.config.save_pretrained(pretrained_dir)
     save_training_state(checkpoint_dir, step, optimizer, scheduler)
 
 
diff --git a/lerobot/record.py b/lerobot/record.py
index 4f3c32c3a..256af2f37 100644
--- a/lerobot/record.py
+++ b/lerobot/record.py
@@ -44,10 +44,6 @@ from pprint import pformat
 import numpy as np
 import rerun as rr
 
-from peft import PeftConfig, PeftModel
-import importlib
-
-
 from lerobot.common.cameras import (  # noqa: F401
     CameraConfig,  # noqa: F401
 )
@@ -130,21 +126,6 @@ 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
@@ -167,23 +148,15 @@ class RecordConfig:
         if policy_path:
             cli_overrides = parser.get_cli_overrides("policy")
 
+            # In case of a PEFT model We assume that the user saved the policy config (`config.json`) alongside the
+            # adapter parameters / config. If they didn't we could instantiate the default configuration for the policy
+            # but we wouldn't know if that is correct. So, in case of a missing config this will simply fail.
+            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            self.policy.pretrained_path = policy_path
+
             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)
-
-                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)
-                self.policy.pretrained_path = policy_path
-
         if self.teleop is None and self.policy is None:
             raise ValueError("Choose a policy, a teleoperator or both to control the robot")
 
@@ -314,6 +287,9 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
     # Load pretrained policy
 
     if cfg.policy.use_peft:
+        from peft import PeftModel
+
+        logging.info("Loading policy's PEFT adapter.")
         # 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
diff --git a/tests/utils/test_train_utils.py b/tests/utils/test_train_utils.py
index b78f6e497..198c5c4ea 100644
--- a/tests/utils/test_train_utils.py
+++ b/tests/utils/test_train_utils.py
@@ -79,6 +79,20 @@ def test_save_checkpoint(mock_save_training_state, tmp_path, optimizer):
     mock_save_training_state.assert_called_once()
 
 
+@patch("lerobot.common.utils.train_utils.save_training_state")
+def test_save_checkpoint_peft(mock_save_training_state, tmp_path, optimizer):
+    policy = Mock()
+    policy.config = Mock()
+    policy.config.save_pretrained = Mock()
+    cfg = Mock()
+    cfg.use_peft = True
+    save_checkpoint(tmp_path, 10, cfg, policy, optimizer)
+    policy.save_pretrained.assert_called_once()
+    cfg.save_pretrained.assert_called_once()
+    policy.config.save_pretrained.assert_called_once()
+    mock_save_training_state.assert_called_once()
+
+
 def test_save_training_state(tmp_path, optimizer, scheduler):
     save_training_state(tmp_path, 10, optimizer, scheduler)
     assert (tmp_path / TRAINING_STATE_DIR).is_dir()