 b/tests/tuning/test_update_config.py
+import pytest
+try:
+    import optuna
+except ImportError:
+    optuna = None
+if optuna is None:
+    pytest.skip("optuna not installed", allow_module_level=True)
+from edsnlp.tune import update_config
+@pytest.fixture
+def minimal_config():
+    return {
+        "train": {
+            "layers": None,
+        },
+        ".lr": {
+            "learning_rate": None,
+        },
+    }
+@pytest.fixture
+def hyperparameters():
+    return {
+        "train.layers": {
+            "type": "int",
+            "low": 2,
+            "high": 8,
+            "step": 2,
+        },
+        "'.lr'.learning_rate": {
+            "alias": "learning_rate",
+            "type": "float",
+            "low": 0.001,
+            "high": 0.1,
+            "log": True,
+        },
+        "train.batch_size": {
+            "alias": "batch_size",
+            "type": "categorical",
+            "choices": [32, 64, 128],
+        },
+    }
+@pytest.fixture
+def hyperparameters_with_invalid_type():
+    return {
+        "train.optimizer": {
+            "type": "string",
+            "choices": ["adam", "sgd"],
+        }
+    }
+@pytest.fixture
+def hyperparameters_with_invalid_path():
+    return {
+        "model.layers": {
+            "type": "int",
+            "low": 2,
+            "high": 8,
+            "step": 2,
+        },
+    }
+@pytest.fixture
+def trial():
+    study = optuna.create_study(direction="maximize")
+    trial = study.ask()
+    return trial
+def test_update_config_with_values(minimal_config, hyperparameters):
+    values = {"learning_rate": 0.05, "train.layers": 6, "batch_size": 64}
+    _, updated_config = update_config(minimal_config, hyperparameters, values=values)
+    assert updated_config[".lr"]["learning_rate"] == values["learning_rate"]
+    assert updated_config["train"]["layers"] == values["train.layers"]
+    assert updated_config["train"]["batch_size"] == values["batch_size"]
+def test_update_config_with_trial(minimal_config, hyperparameters, trial):
+    _, updated_config = update_config(minimal_config, hyperparameters, trial=trial)
+    learning_rate = updated_config[".lr"]["learning_rate"]
+    layers = updated_config["train"]["layers"]
+    batch_size = updated_config["train"]["batch_size"]
+    assert (
+        hyperparameters["'.lr'.learning_rate"]["low"]
+        <= learning_rate
+        <= hyperparameters["'.lr'.learning_rate"]["high"]
+    )
+    assert (
+        hyperparameters["train.layers"]["low"]
+        <= layers
+        <= hyperparameters["train.layers"]["high"]
+    )
+    assert layers % hyperparameters["train.layers"]["step"] == 0
+    assert batch_size in hyperparameters["train.batch_size"]["choices"]
+def test_update_config_raises_error_on_unknown_parameter_type(
+    minimal_config, hyperparameters_with_invalid_type, trial
+):
+    with pytest.raises(
+        ValueError,
+        match="Unknown parameter type 'string' for hyperparameter 'train.optimizer'.",
+    ):
+        update_config(minimal_config, hyperparameters_with_invalid_type, trial=trial)
+def test_update_config_raises_error_on_wrong_path(
+    minimal_config, hyperparameters_with_invalid_path, trial
+):
+    with pytest.raises(KeyError, match="Path 'model' not found in config."):
+        update_config(minimal_config, hyperparameters_with_invalid_path, trial=trial)