diff --git a/dan/manager/training.py b/dan/manager/training.py
index 1c7f157b37329cb56c5dd697c1f549599851a81a..72acce307e2ca0f112584753269c1b41847ed74c 100644
--- a/dan/manager/training.py
+++ b/dan/manager/training.py
@@ -357,7 +357,6 @@ class GenericTrainingManager:
         Load the optimizer of each model
         """
         for model_name in self.models.keys():
-            new_params = dict()
             if (
                 checkpoint
                 and "optimizer_named_params_{}".format(model_name) in checkpoint
@@ -365,16 +364,6 @@ class GenericTrainingManager:
                 self.optimizers_named_params_by_group[model_name] = checkpoint[
                     "optimizer_named_params_{}".format(model_name)
                 ]
-                # for progressively growing models
-                for name, param in self.models[model_name].named_parameters():
-                    existing = False
-                    for gr in self.optimizers_named_params_by_group[model_name]:
-                        if name in gr:
-                            gr[name] = param
-                            existing = True
-                            break
-                    if not existing:
-                        new_params.update({name: param})
             else:
                 self.optimizers_named_params_by_group[model_name] = [
                     dict(),
@@ -420,13 +409,6 @@ class GenericTrainingManager:
                         checkpoint["lr_scheduler_{}_state_dict".format(model_name)]
                     )
 
-            # for progressively growing models, keeping learning rate
-            if checkpoint and new_params:
-                self.optimizers_named_params_by_group[model_name].append(new_params)
-                self.optimizers[model_name].add_param_group(
-                    {"params": list(new_params.values())}
-                )
-
     @staticmethod
     def set_model_learnable(model, learnable=True):
         for p in list(model.parameters()):