diff --git a/python/ray/train/v2/_internal/execution/context.py b/python/ray/train/v2/_internal/execution/context.py
index 37905d8d7be8..337e6cadef2c 100644
--- a/python/ray/train/v2/_internal/execution/context.py
+++ b/python/ray/train/v2/_internal/execution/context.py
@@ -258,6 +258,12 @@ def _upload_checkpoint(
                 persisted_checkpoint = checkpoint_upload_fn(
                     checkpoint, checkpoint_dir_name
                 )
+                if persisted_checkpoint is None or not isinstance(
+                    persisted_checkpoint, ray.train.Checkpoint
+                ):
+                    raise ValueError(
+                        "checkpoint_upload_fn must return a `ray.train.Checkpoint`."
+                    )
             else:
                 persisted_checkpoint = self.storage_context.persist_current_checkpoint(
                     checkpoint, checkpoint_dir_name
diff --git a/python/ray/train/v2/tests/test_async_checkpointing_validation.py b/python/ray/train/v2/tests/test_async_checkpointing_validation.py
index d438274984ee..a5dc9f896eb8 100644
--- a/python/ray/train/v2/tests/test_async_checkpointing_validation.py
+++ b/python/ray/train/v2/tests/test_async_checkpointing_validation.py
@@ -369,6 +369,26 @@ def train_fn():
     }
 
 
+def test_checkpoint_upload_fn_returns_checkpoint():
+    def train_fn():
+        with create_dict_checkpoint({}) as checkpoint:
+            ray.train.report(
+                metrics={},
+                checkpoint=checkpoint,
+                checkpoint_upload_fn=lambda x, y: None,
+            )
+
+    trainer = DataParallelTrainer(
+        train_fn,
+        scaling_config=ScalingConfig(num_workers=1),
+    )
+    with pytest.raises(
+        WorkerGroupError,
+        match="checkpoint_upload_fn must return a `ray.train.Checkpoint`",
+    ):
+        trainer.fit()
+
+
 def test_get_all_reported_checkpoints_all_consistency_modes():
     signal_actor = create_remote_signal_actor(ray).remote()