diff --git a/opennmt/tests/runner_test.py b/opennmt/tests/runner_test.py
index b826161ea..87ecbec49 100644
--- a/opennmt/tests/runner_test.py
+++ b/opennmt/tests/runner_test.py
@@ -115,6 +115,10 @@ def testTrain(self, pass_model_builder):
     with open(en_file) as f:
       self.assertEqual(next(f).strip(), "a t z m o n")
 
+    # Continue the training without updating max_step
+    with self.assertRaises(RuntimeError, match="max_step"):
+      runner.train()
+
   @test_util.run_with_two_cpu_devices
   def testTrainDistribute(self):
     ar_file, en_file  = self._makeTransliterationData()
diff --git a/opennmt/training.py b/opennmt/training.py
index f4b870b20..a685874c1 100644
--- a/opennmt/training.py
+++ b/opennmt/training.py
@@ -73,11 +73,13 @@ def __call__(self,
         https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage.
     """
     if max_step is not None and self._optimizer.iterations.numpy() >= max_step:
-      tf.get_logger().warning("Model already reached max_step = %d. Exiting.", max_step)
-      return
+      raise RuntimeError("The training already reached max_step (%d). If you "
+                         "want to continue the training, you should increase the "
+                         "max_step value in the training parameters." % max_step)
     if evaluator is not None and evaluator.should_stop():
-      tf.get_logger().warning("Early stopping conditions are already met. Exiting.")
-      return
+      raise RuntimeError("The early stopping conditions are already met. If you "
+                         "want to continue the training, you should update your "
+                         "early stopping parameters.")
 
     self._gradient_accumulator.reset()
     self._words_counters.clear()