diff --git a/hbw/config/defaults_and_groups.py b/hbw/config/defaults_and_groups.py
index e9b31582..547815ca 100644
--- a/hbw/config/defaults_and_groups.py
+++ b/hbw/config/defaults_and_groups.py
@@ -6,6 +6,15 @@
 from columnflow.tasks.framework.base import RESOLVE_DEFAULT
 
 
+def default_selector(cls, container, task_params):
+    if container.has_tag("is_sl"):
+        selector = "sl"
+    elif container.has_tag("is_dl"):
+        selector = "dl"
+
+    return selector
+
+
 def default_ml_model(cls, container, task_params):
     """ Function that chooses the default_ml_model based on the inference_model if given """
     # for most tasks, do not use any default ml model
@@ -13,7 +22,7 @@ def default_ml_model(cls, container, task_params):
 
     # the ml_model parameter is only used by `MLTraining` and `MLEvaluation`, therefore use some default
     # NOTE: default_ml_model does not work for the MLTraining task
-    if cls.task_family in ("cf.MLTraining", "cf.MLEvaulation"):
+    if cls.task_family in ("cf.MLTraining", "cf.MLEvaulation", "cf.MergeMLEvents", "cf.PrepareMLEvents"):
         # TODO: we might want to distinguish between two default ML models (sl vs dl)
         default_ml_model = "dense_default"
 
@@ -34,10 +43,14 @@ def default_ml_model(cls, container, task_params):
 
 def default_producers(cls, container, task_params):
     """ Default producers chosen based on the Inference model and the ML Model """
+    dataset_inst = task_params.get("dataset_inst", None)
 
     # per default, use the ml_inputs and event_weights
     # TODO: we might need two ml_inputs producers in the future (sl vs dl)
-    default_producers = ["ml_inputs", "event_weights"]
+    default_producers = ["ml_inputs"]
+    if dataset_inst and dataset_inst.is_mc:
+        # run event weights producer only if it's a MC dataset
+        default_producers.append("event_weights")
 
     # check if a ml_model has been set
     ml_model = task_params.get("mlmodel", None) or task_params.get("mlmodels", None)
@@ -87,7 +100,7 @@ def set_config_defaults_and_groups(config_inst):
     # TODO: the default dataset is currently still being set up by the law.cfg
     config_inst.x.default_dataset = default_signal_dataset = f"{default_signal_process}_{signal_generator}"
     config_inst.x.default_calibrator = "skip_jecunc"
-    config_inst.x.default_selector = f"{signal_tag}"
+    config_inst.x.default_selector = default_selector
     config_inst.x.default_producer = default_producers
     config_inst.x.default_ml_model = default_ml_model
     config_inst.x.default_inference_model = "default"
diff --git a/hbw/ml/dense_classifier.py b/hbw/ml/dense_classifier.py
index f1e5ccfb..45e1aab8 100644
--- a/hbw/ml/dense_classifier.py
+++ b/hbw/ml/dense_classifier.py
@@ -160,7 +160,7 @@ def training_configs(self, requested_configs: Sequence[str]) -> list[str]:
             return list(requested_configs)
         else:
             # use config_2017 per default
-            return ["config_2017"]
+            return ["c17"]
 
     def training_calibrators(self, config_inst: od.Config, requested_calibrators: Sequence[str]) -> list[str]:
         # fix MLTraining Phase Space
@@ -168,7 +168,7 @@ def training_calibrators(self, config_inst: od.Config, requested_calibrators: Se
 
     def training_selector(self, config_inst: od.Config, requested_selector: str) -> str:
         # fix MLTraining Phase Space
-        return "default"
+        return "sl" if self.config_inst.has_tag("is_sl") else "dl"
 
     def training_producers(self, config_inst: od.Config, requested_producers: Sequence[str]) -> list[str]:
         # fix MLTraining Phase Space