diff --git a/higher/smart_aug/test_dataug.py b/higher/smart_aug/test_dataug.py
index 9858d4e..ce1cc0e 100755
--- a/higher/smart_aug/test_dataug.py
+++ b/higher/smart_aug/test_dataug.py
@@ -8,7 +8,7 @@ from dataug import *
 from train_utils import *
 from transformations import TF_loader
 
-postfix=''
+postfix='-metaScheduler'
 TF_loader=TF_loader()
 
 device = torch.device('cuda') #Select device to use
@@ -40,9 +40,10 @@ if __name__ == "__main__":
     optim_param={
         'Meta':{
             'optim':'Adam',
-            'lr':1e-2, #1e-2
+            'lr':1e-4, #1e-2
             'epoch_start': 2, #0 / 2 (Resnet?)
             'reg_factor': 0.001,
+            'scheduler': 'multiStep', #None, 'multiStep'
         },
         'Inner':{
             'optim': 'SGD',
@@ -138,7 +139,7 @@ if __name__ == "__main__":
              inner_it=n_inner_iter, 
              dataug_epoch_start=dataug_epoch_start, 
              opt_param=optim_param,
-             print_freq=10, 
+             print_freq=20, 
              unsup_loss=1, 
              hp_opt=False,
              save_sample_freq=None)
diff --git a/higher/smart_aug/train_utils.py b/higher/smart_aug/train_utils.py
index ce4295e..1df18c8 100755
--- a/higher/smart_aug/train_utils.py
+++ b/higher/smart_aug/train_utils.py
@@ -292,6 +292,14 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
                 hyper_param += [param_group[param]]
     meta_opt = torch.optim.Adam(hyper_param, lr=opt_param['Meta']['lr']) #lr=1e-2
 
+    meta_scheduler=None
+    if opt_param['Meta']['scheduler']=='multiStep':
+        meta_scheduler=torch.optim.lr_scheduler.MultiStepLR(meta_opt, 
+            milestones=[int(epochs/3), int(epochs*2/3), int(epochs*2.7/3)], 
+            gamma=10)
+    elif opt_param['Meta']['scheduler'] is not None:
+        raise ValueError("Lr scheduler unknown : %s"%opt_param['Meta']['scheduler'])
+
     model.train()
     meta_opt.zero_grad()
 
@@ -356,12 +364,15 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
                 
         tf = time.perf_counter()
 
+        #Schedulers
         if inner_scheduler is not None:
             inner_scheduler.step()
             #Transfer inner_opt lr to diffopt
             for diff_param_group in diffopt.param_groups:
                 for param_group in inner_opt.param_groups:
                     diff_param_group['lr'] = param_group['lr']
+        if meta_scheduler is not None:
+            meta_scheduler.step()
 
         if (save_sample_freq and epoch%save_sample_freq==0): #Data sample saving
                 try: