diff --git a/sciencebeam_gym/trainer/task.py b/sciencebeam_gym/trainer/task.py
index 4fb3486864ba4549d221057a45c2f0f67137078e..a8401eda233ba05de481c50f7e32ba088474638b 100644
--- a/sciencebeam_gym/trainer/task.py
+++ b/sciencebeam_gym/trainer/task.py
@@ -22,7 +22,6 @@ from sciencebeam_gym.trainer.evaluator import Evaluator
 from sciencebeam_gym.trainer.util import (
   CustomSupervisor,
   SimpleStepScheduler,
-  override_if_not_in_args,
   get_graph_size
 )
 
@@ -499,14 +498,17 @@ def run(model, argv):
   parser.add_argument(
     '--max_steps',
     type=int,
+    default=1000
   )
   parser.add_argument(
     '--batch_size',
     type=int,
+    default=100,
     help='Number of examples to be processed per mini-batch.'
   )
   parser.add_argument(
-    '--eval_set_size', type=int, help='Number of examples in the eval set.'
+    '--eval_set_size', type=int, default=370,
+    help='Number of examples in the eval set.'
   )
   parser.add_argument(
     '--qualitative_set_size',
@@ -690,12 +692,6 @@ def main(_):
   model_factory = get_model_factory(args.model)
 
   model, task_args = model_factory.create_model(other_args)
-  override_if_not_in_args('--max_steps', '1000', task_args)
-  override_if_not_in_args('--batch_size', '100', task_args)
-  override_if_not_in_args('--eval_set_size', '370', task_args)
-  override_if_not_in_args('--eval_interval_secs', '2', task_args)
-  override_if_not_in_args('--log_interval_secs', '2', task_args)
-  override_if_not_in_args('--min_train_eval_rate', '2', task_args)
   run(model, task_args)
 
 if __name__ == '__main__':
diff --git a/sciencebeam_gym/trainer/util.py b/sciencebeam_gym/trainer/util.py
index 810082e6a0ea174eec45577dd3fe12303a06808d..57d5dddcd12c58a311b9199067dbdc69d3884f83 100644
--- a/sciencebeam_gym/trainer/util.py
+++ b/sciencebeam_gym/trainer/util.py
@@ -139,11 +139,6 @@ def read_examples(input_files, shuffle, num_epochs=None):
 
   return example_id, encoded_example
 
-def override_if_not_in_args(flag, argument, args):
-  """Checks if flags is in args, and if not it adds the flag to args."""
-  if flag not in args:
-    args.extend([flag, argument])
-
 def loss(loss_value):
   """Calculates aggregated mean loss."""
   total_loss = tf.Variable(0.0, False)