diff --git a/botorch/optim/optimize_homotopy.py b/botorch/optim/optimize_homotopy.py
index 87ba7b41cc..5f218eeca6 100644
--- a/botorch/optim/optimize_homotopy.py
+++ b/botorch/optim/optimize_homotopy.py
@@ -85,7 +85,7 @@ def optimize_acqf_homotopy(
         ("optimize_acqf_loop_kwargs", optimize_acqf_loop_kwargs),
         ("optimize_acqf_final_kwargs", optimize_acqf_final_kwargs),
     ]:
-        if "return_best_only" in kwarg_dict:
+        if kwarg_dict.get("return_best_only", None) is not False:
             warnings.warn(
                 f"`return_best_only` is set to True in `{kwarg_dict_name}`, override to False."
             )
diff --git a/test/optim/test_homotopy.py b/test/optim/test_homotopy.py
index e2f2d1c611..a531e525fb 100644
--- a/test/optim/test_homotopy.py
+++ b/test/optim/test_homotopy.py
@@ -125,7 +125,10 @@ def test_optimize_acqf_homotopy(self):
             bounds=torch.tensor([[-10], [5]]).to(**tkwargs),
             homotopy=Homotopy(homotopy_parameters=[hp]),
             optimize_acqf_loop_kwargs=optimize_acqf_core_kwargs,
-            optimize_acqf_final_kwargs=optimize_acqf_core_kwargs.update({"post_processing_func":lambda x: x.round()}),
+            optimize_acqf_final_kwargs={
+                **optimize_acqf_core_kwargs,
+                "post_processing_func": lambda x: x.round(),
+            },
         )
         self.assertEqual(candidate, torch.zeros(1, **tkwargs))
         self.assertEqual(acqf_val, 5 * torch.ones(1, **tkwargs))
@@ -141,8 +144,11 @@ def test_optimize_acqf_homotopy(self):
             acq_function=acqf,
             bounds=torch.tensor([[-10, -10], [5, 5]]).to(**tkwargs),
             homotopy=Homotopy(homotopy_parameters=[hp]),
-            optimize_acqf_loop_kwargs=optimize_acqf_core_kwargs.update({"fixed_features":fixed_features}),
-            optimize_acqf_final_kwargs=optimize_acqf_core_kwargs
+            optimize_acqf_loop_kwargs={
+                **optimize_acqf_core_kwargs,
+                "fixed_features": fixed_features,
+            },
+            optimize_acqf_final_kwargs=optimize_acqf_core_kwargs,
         )
         self.assertEqual(candidate[0, 0], torch.tensor(1, **tkwargs))
 
@@ -153,8 +159,11 @@ def test_optimize_acqf_homotopy(self):
             acq_function=acqf,
             bounds=torch.tensor([[-10, -10], [5, 5]]).to(**tkwargs),
             homotopy=Homotopy(homotopy_parameters=[hp]),
-            optimize_acqf_loop_kwargs=optimize_acqf_core_kwargs.update({"fixed_features":fixed_features}),
-            optimize_acqf_final_kwargs=optimize_acqf_core_kwargs
+            optimize_acqf_loop_kwargs={
+                **optimize_acqf_core_kwargs,
+                "fixed_features": fixed_features,
+            },
+            optimize_acqf_final_kwargs=optimize_acqf_core_kwargs,
         )
         self.assertEqual(candidate.shape, torch.Size([3, 2]))
         self.assertEqual(acqf_val.shape, torch.Size([3]))
@@ -207,8 +216,8 @@ def test_optimize_acqf_homotopy_pruning(self, prune_candidates_mock):
         model = GenericDeterministicModel(f=lambda x: 5 - (x - p) ** 2)
         acqf = PosteriorMean(model=model)
         optimize_acqf_core_kwargs = {
-            "num_restarts":2,
-            "raw_samples":16,
+            "num_restarts": 4,
+            "raw_samples": 16,
         }
 
         candidate, acqf_val = optimize_acqf_homotopy(
@@ -217,7 +226,10 @@ def test_optimize_acqf_homotopy_pruning(self, prune_candidates_mock):
             bounds=torch.tensor([[-10], [5]]).to(**tkwargs),
             homotopy=Homotopy(homotopy_parameters=[hp]),
             optimize_acqf_loop_kwargs=optimize_acqf_core_kwargs,
-            optimize_acqf_final_kwargs=optimize_acqf_core_kwargs.update({"post_processing_func":lambda x: x.round()}),
+            optimize_acqf_final_kwargs={
+                **optimize_acqf_core_kwargs,
+                "post_processing_func": lambda x: x.round(),
+            },
         )
         # First time we expect to call `prune_candidates` with 4 candidates
         self.assertEqual(