diff --git a/src/itwinai/torch/trainer.py b/src/itwinai/torch/trainer.py
index 68216a00..28079205 100644
--- a/src/itwinai/torch/trainer.py
+++ b/src/itwinai/torch/trainer.py
@@ -1468,18 +1468,23 @@ def _set_train_loop_config(self):
         train_loop_config = self.config.get("train_loop_config", {})
 
         if train_loop_config:
-            for name, param in train_loop_config.items():
-                try:
+            try:
+                for name, param in train_loop_config.items():
                     if isinstance(param, dict):
+                        # Convert specific keys to float if necessary
+                        for key in ["lower", "upper", "mean", "std"]:
+                            if key in param:
+                                param[key] = float(param[key])
+
                         param_type = param.pop("type")
-                        # Dynamically call corresponding tune method
                         param = getattr(tune, param_type)(**param)
                         train_loop_config[name] = param
-                except AttributeError:
-                    raise ValueError(
-                        f"{param} could not be set. Check that this parameter type is "
-                        "supported by Ray Tune or the itwinai TrainingConfiguration."
-                    )
+
+            except AttributeError:
+                print(
+                    f"{param} could not be set. Check that this parameter type is "
+                    "supported by Ray Tune or the itwinai TrainingConfiguration."
+                )
         else:
             print(
                 "WARNING: No training_loop_config detected. "
diff --git a/use-cases/virgo/config.yaml b/use-cases/virgo/config.yaml
index 30ce4dda..9ad9ef89 100644
--- a/use-cases/virgo/config.yaml
+++ b/use-cases/virgo/config.yaml
@@ -126,12 +126,12 @@ ray_training_pipeline:
                   GPU: 1
             tune_config:
               num_samples: 2
-              # scheduler:
-              #   name: asha
-              #   max_t: 5
-              #   grace_period: 2
-              #   reduction_factor: 6
-              #   brackets: 1
+              scheduler:
+                name: asha
+                max_t: 5
+                grace_period: 2
+                reduction_factor: 6
+                brackets: 1
             run_config:
               storage_path: ray_checkpoints
               name: Virgo-HPO-Experiment
@@ -144,10 +144,10 @@ ray_training_pipeline:
                 lower: 1e-5
                 upper: 1e-3
               num_epochs: 5
-            generator: simple #unet
-            loss: l1
-            save_best: false
-            shuffle_train: true
+              generator: simple #unet
+              loss: l1
+              save_best: false
+              shuffle_train: true
           strategy: ${strategy}
           random_seed: 17
           logger: