diff --git a/pytorch/config.yaml b/pytorch/config.yaml
index c103e26..2e24416 100644
--- a/pytorch/config.yaml
+++ b/pytorch/config.yaml
@@ -34,8 +34,8 @@ modelParam:
     divL1Lambda: 0
     # MSE of long term divergence
     # If > 0, implements the Long Term divergence concept from FluidNet
-    divLongTermLambda: 0
-    # Time step: default simulation timestep. 
+    divLongTermLambda: 1
+    # Time step: default simulation timestep only when long term divergence is active. 
     dt: 0.1
     # buoyancyScale : Buoyancy forces scale
     # gravityScale : Gravity forces scale
@@ -96,7 +96,7 @@ modelParam:
 numWorkers: 3
 # If true, dataset is preprocessed and programs exists.
 # Preprocessing is automatic if no previous preproc is detected on current dataset.
-preprocOnly: false 
+preprocOriginalFluidNetDataOnly: false 
 # printTraining : Debug options for training.
 # Prints or shows validation dataset and compares net
 # output to GT.
diff --git a/pytorch/fluid_net_train.py b/pytorch/fluid_net_train.py
index 62ec90e..78ffe76 100644
--- a/pytorch/fluid_net_train.py
+++ b/pytorch/fluid_net_train.py
@@ -96,7 +96,7 @@
 conf['shuffleTraining'] = not arguments.noShuffle
 
 # Preprocessing dataset message (will exit after preproc)
-if (conf['preprocOnly']):
+if (conf['preprocOriginalFluidNetDataOnly']):
     print('Running preprocessing only')
     resume = False
 
@@ -106,7 +106,7 @@
 tr = lib.FluidNetDataset(conf, 'tr', save_dt=4, resume=resume)
 te = lib.FluidNetDataset(conf, 'te', save_dt=4, resume=resume)
 
-if (conf['preprocOnly']):
+if (conf['preprocOriginalFluidNetDataOnly']):
     sys.exit()
 
 # We create two conf dicts, general params and model params.