diff --git a/plugins/flytekit-kf-pytorch/flytekitplugins/kfpytorch/task.py b/plugins/flytekit-kf-pytorch/flytekitplugins/kfpytorch/task.py
index 832ab17a1c..cfe2be1ad8 100644
--- a/plugins/flytekit-kf-pytorch/flytekitplugins/kfpytorch/task.py
+++ b/plugins/flytekit-kf-pytorch/flytekitplugins/kfpytorch/task.py
@@ -15,7 +15,7 @@
 import flytekit
 from flytekit import PythonFunctionTask, Resources, lazy_module
 from flytekit.configuration import SerializationSettings
-from flytekit.core.context_manager import OutputMetadata
+from flytekit.core.context_manager import FlyteContextManager, OutputMetadata
 from flytekit.core.pod_template import PodTemplate
 from flytekit.core.resources import convert_resources_to_resource_model
 from flytekit.exceptions.user import FlyteRecoverableException
@@ -429,13 +429,18 @@ def fn_partial():
                 """Closure of the task function with kwargs already bound."""
                 try:
                     return_val = self._task_function(**kwargs)
+                    core_context = FlyteContextManager.current_context()
+                    omt = core_context.output_metadata_tracker
+                    om = None
+                    if omt:
+                        om = omt.get(return_val)
                 except Exception as e:
                     # See explanation in `create_recoverable_error_file` why we check
                     # for recoverable errors here in the worker processes.
                     if isinstance(e, FlyteRecoverableException):
                         create_recoverable_error_file()
                     raise
-                return ElasticWorkerResult(return_value=return_val, decks=flytekit.current_context().decks, om=None)
+                return ElasticWorkerResult(return_value=return_val, decks=flytekit.current_context().decks, om=om)
 
             launcher_target_func = fn_partial
             launcher_args = ()
@@ -470,7 +475,8 @@ def fn_partial():
                 if not isinstance(deck, flytekit.deck.deck.TimeLineDeck):
                     ctx.decks.append(deck)
             if out[0].om:
-                ctx.output_metadata_tracker.add(out[0].return_value, out[0].om)
+                core_context = FlyteContextManager.current_context()
+                core_context.output_metadata_tracker.add(out[0].return_value, out[0].om)
 
             return out[0].return_value
         else:
diff --git a/plugins/flytekit-kf-pytorch/tests/test_elastic_task.py b/plugins/flytekit-kf-pytorch/tests/test_elastic_task.py
index b56fc0aa08..39f1e0bb80 100644
--- a/plugins/flytekit-kf-pytorch/tests/test_elastic_task.py
+++ b/plugins/flytekit-kf-pytorch/tests/test_elastic_task.py
@@ -2,6 +2,10 @@
 import typing
 from dataclasses import dataclass
 from unittest import mock
+from typing_extensions import Annotated, cast
+from flytekitplugins.kfpytorch.task import Elastic
+
+from flytekit import Artifact
 
 import pytest
 import torch
@@ -11,6 +15,7 @@
 
 import flytekit
 from flytekit import task, workflow
+from flytekit.core.context_manager import FlyteContext, FlyteContextManager, ExecutionState, ExecutionParameters, OutputMetadataTracker
 from flytekit.configuration import SerializationSettings
 from flytekit.exceptions.user import FlyteRecoverableException
 
@@ -159,6 +164,41 @@ def wf():
     assert "Hello Flyte Deck viewer from worker process 0" in test_deck.html
 
 
+class Card(object):
+    def __init__(self, text: str):
+        self.text = text
+
+    def serialize_to_string(self, ctx: FlyteContext, variable_name: str):
+        print(f"In serialize_to_string: {id(ctx)}")
+        return "card", "card"
+
+
+@pytest.mark.parametrize("start_method", ["spawn", "fork"])
+def test_output_metadata_passing(start_method: str) -> None:
+    ea = Artifact(name="elastic-artf")
+
+    @task(
+        task_config=Elastic(start_method=start_method),
+    )
+    def train2() -> Annotated[str, ea]:
+        return ea.create_from("hello flyte", Card("## card"))
+
+    @workflow
+    def wf():
+        train2()
+
+    ctx = FlyteContext.current_context()
+    omt = OutputMetadataTracker()
+    with FlyteContextManager.with_context(
+            ctx.with_execution_state(ctx.new_execution_state().with_params(mode=ExecutionState.Mode.LOCAL_TASK_EXECUTION)).with_output_metadata_tracker(omt)
+    ) as child_ctx:
+        cast(ExecutionParameters, child_ctx.user_space_params)._decks = []
+        # call execute directly so as to be able to get at the same FlyteContext object.
+        res = train2.execute()
+        om = child_ctx.output_metadata_tracker.get(res)
+        assert len(om.additional_items) == 1
+
+
 @pytest.mark.parametrize(
     "recoverable,start_method",
     [