diff --git a/darts/models/forecasting/pl_forecasting_module.py b/darts/models/forecasting/pl_forecasting_module.py
index 6318afb4cd..b8c96623a9 100644
--- a/darts/models/forecasting/pl_forecasting_module.py
+++ b/darts/models/forecasting/pl_forecasting_module.py
@@ -400,11 +400,18 @@ def _update_metrics(self, output, target, metrics):
             return
 
         if self.likelihood:
-            metrics.update(self.likelihood.sample(output), target)
+            pred = self.likelihood.sample(output)
         else:
             # If there's no likelihood, nr_params=1, and we need to squeeze out the
             # last dimension of model output, for properly computing the metric.
-            metrics.update(output.squeeze(dim=-1), target)
+            pred = output.squeeze(dim=-1)
+
+        # torch metrics require 2D targets of shape (batch size * ocl, num targets)
+        if self.n_targets > 1:
+            target = target.reshape(-1, self.n_targets)
+            pred = pred.reshape(-1, self.n_targets)
+
+        metrics.update(pred, target)
 
     def _compute_metrics(self, metrics):
         if not len(metrics):
diff --git a/darts/tests/models/forecasting/test_torch_forecasting_model.py b/darts/tests/models/forecasting/test_torch_forecasting_model.py
index 2668d8d767..9ae63e80f7 100644
--- a/darts/tests/models/forecasting/test_torch_forecasting_model.py
+++ b/darts/tests/models/forecasting/test_torch_forecasting_model.py
@@ -1310,7 +1310,7 @@ def test_metrics(self):
             10,
             10,
             n_epochs=1,
-            torch_metrics=metric,
+            torch_metrics=metric_collection,
             pl_trainer_kwargs=model_kwargs,
         )
         model.fit(self.multivariate_series)