diff --git a/spacy/training/loop.py b/spacy/training/loop.py
index eecb3e273..c8df57a3c 100644
--- a/spacy/training/loop.py
+++ b/spacy/training/loop.py
@@ -28,7 +28,7 @@ def train(
     use_gpu: int = -1,
     stdout: IO = sys.stdout,
     stderr: IO = sys.stderr,
-) -> None:
+) -> Tuple["Language", Optional[Path]]:
     """Train a pipeline.
 
     nlp (Language): The initialized nlp object with the full config.
@@ -40,7 +40,7 @@ def train(
     stderr (file): A second file-like object to write output messages. To disable
         printing, set to io.StringIO.
 
-    RETURNS (Path / None): The path to the final exported model.
+    RETURNS (tuple): The final nlp object and the path to the exported model.
     """
     # We use no_print here so we can respect the stdout/stderr options.
     msg = Printer(no_print=True)
@@ -105,17 +105,18 @@ def train(
         raise e
     finally:
         finalize_logger()
+        if optimizer.averages:
+            nlp.use_params(optimizer.averages)
         if output_path is not None:
             final_model_path = output_path / DIR_MODEL_LAST
-            if optimizer.averages:
-                with nlp.use_params(optimizer.averages):
-                    nlp.to_disk(final_model_path)
-            else:
-                nlp.to_disk(final_model_path)
+            nlp.to_disk(final_model_path)
             # This will only run if we don't hit an error
             stdout.write(
                 msg.good("Saved pipeline to output directory", final_model_path) + "\n"
             )
+            return (nlp, final_model_path)
+        else:
+            return (nlp, None)
 
 
 def train_while_improving(