diff --git a/src/graphnet/models/easy_model.py b/src/graphnet/models/easy_model.py
index b43b7d63e..90d48b448 100644
--- a/src/graphnet/models/easy_model.py
+++ b/src/graphnet/models/easy_model.py
@@ -36,8 +36,23 @@ def __init__(
         scheduler_class: Optional[type] = None,
         scheduler_kwargs: Optional[Dict] = None,
         scheduler_config: Optional[Dict] = None,
+        also_log_train_loss_per_step: bool = False,
     ) -> None:
-        """Construct `StandardModel`."""
+        """Construct `StandardModel`.
+
+        Args:
+            tasks: Task(s) appended as the head(s) of the model, defining
+                the prediction target(s) and loss(es).
+            optimizer_class: Optimizer class used during training.
+            optimizer_kwargs: Keyword arguments passed to `optimizer_class`.
+            scheduler_class: Learning-rate scheduler class. If `None`, no
+                scheduler is used.
+            scheduler_kwargs: Keyword arguments passed to `scheduler_class`.
+            scheduler_config: Additional configuration for how the scheduler
+                is invoked by PyTorch Lightning (e.g. `interval`, `frequency`).
+            also_log_train_loss_per_step: If `True`, additionally logs the
+                per-batch training loss under `train_loss_step`.
+        """
         # Base class constructor
         super().__init__(name=__name__, class_name=self.__class__.__name__)
 
@@ -52,6 +67,7 @@ def __init__(
         self._scheduler_class = scheduler_class
         self._scheduler_kwargs = scheduler_kwargs or dict()
         self._scheduler_config = scheduler_config or dict()
+        self._also_log_train_loss_per_step = also_log_train_loss_per_step
 
         self.validate_tasks()
 
@@ -243,15 +259,26 @@ def training_step(
         if isinstance(train_batch, Data):
             train_batch = [train_batch]
         loss = self.shared_step(train_batch, batch_idx)
+        batch_size = self._get_batch_size(train_batch)
         self.log(
             "train_loss",
             loss,
-            batch_size=self._get_batch_size(train_batch),
+            batch_size=batch_size,
             prog_bar=True,
             on_epoch=True,
             on_step=False,
             sync_dist=True,
         )
+        if self._also_log_train_loss_per_step:
+            self.log(
+                "train_loss_step",
+                loss,
+                batch_size=batch_size,
+                prog_bar=False,
+                on_epoch=False,
+                on_step=True,
+                sync_dist=False,
+            )
 
         current_lr = self.trainer.optimizers[0].param_groups[0]["lr"]
         self.log("lr", current_lr, prog_bar=True, on_step=True)