diff --git a/docs/guide/export.rst b/docs/guide/export.rst
index 88a02fe8b..a37fb894f 100644
--- a/docs/guide/export.rst
+++ b/docs/guide/export.rst
@@ -100,6 +100,7 @@ If you are using PyTorch 2.0+ and ONNX Opset 14+, you can easily export SB3 poli
   with th.no_grad():
       print(model.policy(th.as_tensor(observation), deterministic=True))
 
+For exporting ``MultiInputPolicy``, please have a look at `GH#1873 <https://github.com/DLR-RM/stable-baselines3/issues/1873#issuecomment-2710776085>`_.
 
 For SAC the procedure is similar. The example shown only exports the actor network as the actor is sufficient to roll out the trained policies.
 
diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index c02a185b3..bb61e2bfd 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -44,6 +44,7 @@ Documentation:
 - Clarify the use of Gym wrappers with ``make_vec_env`` in the section on Vectorized Environments (@pstahlhofen)
 - Updated callback doc for ``EveryNTimesteps``
 - Added doc on how to set env attributes via ``VecEnv`` calls
+- Added ONNX export example for ``MultiInputPolicy`` (@darkopetrovic)
 
 
 Release 2.5.0 (2025-01-27)