Add test checking that PEFT actually reduces params

tests/test_cli.py

@@ -99,6 +99,41 @@ def test_peft_training_works(policy_type, tmp_path):
     assert set(found_keys) == set(fully_trained_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}"
+
+    def dummy_update_policy(
+        train_metrics,
+        policy,
+        batch,
+        optimizer,
+        grad_clip_norm: float,
+        accelerator,
+        **kwargs
+    ):
+        params_total = sum(p.numel() for p in policy.parameters())
+        params_trainable = sum(p.numel() for p in policy.parameters() if p.requires_grad)
+
+        assert params_total > params_trainable
+
+        return train_metrics, {}
+
+    with patch("lerobot.scripts.lerobot_train.update_policy", dummy_update_policy):
+        lerobot_train(
+            [
+                f"--policy.type={policy_type}",
+                "--policy.push_to_hub=false",
+                "--peft.method=LORA",
+                "--dataset.repo_id=lerobot/pusht",
+                "--dataset.episodes=[0, 1]",
+                "--steps=1",
+                f"--output_dir={output_dir}",
+            ]
+        )
+
+
 class DummyRobot:
     name = "dummy"
     cameras = []