diff --git a/botorch/acquisition/multi_objective/utils.py b/botorch/acquisition/multi_objective/utils.py
index 369c0e6a5c..30448b587b 100644
--- a/botorch/acquisition/multi_objective/utils.py
+++ b/botorch/acquisition/multi_objective/utils.py
@@ -154,7 +154,7 @@ def prune_inferior_points_multi_objective(
     probs = pareto_mask.to(dtype=X.dtype).mean(dim=0)
     idcs = probs.nonzero().view(-1)
     if idcs.shape[0] > max_points:
-        counts, order_idcs = torch.sort(probs, descending=True)
+        counts, order_idcs = torch.sort(probs, stable=True, descending=True)
         idcs = order_idcs[:max_points]
     effective_n_w = obj_vals.shape[-2] // X.shape[-2]
     idcs = (idcs / effective_n_w).long().unique()
diff --git a/botorch/acquisition/utils.py b/botorch/acquisition/utils.py
index 198228409a..ae4f054321 100644
--- a/botorch/acquisition/utils.py
+++ b/botorch/acquisition/utils.py
@@ -335,15 +335,16 @@ def prune_inferior_points(
         marginalize_dim=marginalize_dim,
     )
     if infeas.any():
-        # set infeasible points to worse than worst objective
-        # across all samples
+        # set infeasible points to worse than worst objective across all samples
+        # Use clone() here to avoid deprecated `index_put_` on an expanded tensor
+        obj_vals = obj_vals.clone()
         obj_vals[infeas] = obj_vals.min() - 1
 
     is_best = torch.argmax(obj_vals, dim=-1)
     idcs, counts = torch.unique(is_best, return_counts=True)
 
     if len(idcs) > max_points:
-        counts, order_idcs = torch.sort(counts, descending=True)
+        counts, order_idcs = torch.sort(counts, stable=True, descending=True)
         idcs = order_idcs[:max_points]
 
     return X[idcs]
diff --git a/test/acquisition/multi_objective/test_utils.py b/test/acquisition/multi_objective/test_utils.py
index acdfddbc95..793c936b61 100644
--- a/test/acquisition/multi_objective/test_utils.py
+++ b/test/acquisition/multi_objective/test_utils.py
@@ -46,7 +46,7 @@ def test_get_default_partitioning_alpha(self):
 
 
 class DummyMCMultiOutputObjective(MCMultiOutputObjective):
-    def forward(self, samples: Tensor) -> Tensor:
+    def forward(self, samples: Tensor, X: Tensor | None) -> Tensor:
         return samples
 
 
@@ -130,13 +130,12 @@ def test_prune_inferior_points_multi_objective(self):
                 X_pruned = prune_inferior_points_multi_objective(
                     model=mm, X=X, ref_point=ref_point, max_frac=2 / 3
                 )
-            if self.device.type == "cuda":
-                # sorting has different order on cuda
-                self.assertTrue(
-                    torch.equal(X_pruned, X[[2, 1]]) or torch.equal(X_pruned, X[[1, 2]])
+            self.assertTrue(
+                torch.equal(
+                    torch.sort(X_pruned, stable=True).values,
+                    torch.sort(X[:2], stable=True).values,
                 )
-            else:
-                self.assertTrue(torch.equal(X_pruned, X[:2]))
+            )
             # test that zero-probability is in fact pruned
             samples[2, 0, 0] = 10
             with mock.patch.object(MockPosterior, "rsample", return_value=samples):
@@ -276,10 +275,7 @@ def test_random_search_optimizer(self):
         input_dim = 3
         num_initial = 5
         tkwargs = {"device": self.device}
-        optimizer_kwargs = {
-            "pop_size": 1000,
-            "max_tries": 5,
-        }
+        optimizer_kwargs = {"pop_size": 1000, "max_tries": 5}
 
         for (
             dtype,
@@ -350,10 +346,7 @@ def test_sample_optimal_points(self):
         input_dim = 3
         num_initial = 5
         tkwargs = {"device": self.device}
-        optimizer_kwargs = {
-            "pop_size": 100,
-            "max_tries": 1,
-        }
+        optimizer_kwargs = {"pop_size": 100, "max_tries": 1}
         num_samples = 2
         num_points = 1
 
diff --git a/test/acquisition/test_utils.py b/test/acquisition/test_utils.py
index c8a6484cca..61845a387a 100644
--- a/test/acquisition/test_utils.py
+++ b/test/acquisition/test_utils.py
@@ -270,11 +270,12 @@ def test_prune_inferior_points(self):
             with mock.patch.object(MockPosterior, "rsample", return_value=samples):
                 mm = MockModel(MockPosterior(samples=samples))
                 X_pruned = prune_inferior_points(model=mm, X=X, max_frac=2 / 3)
-            if self.device.type == "cuda":
-                # sorting has different order on cuda
-                self.assertTrue(torch.equal(X_pruned, torch.stack([X[2], X[1]], dim=0)))
-            else:
-                self.assertTrue(torch.equal(X_pruned, X[:2]))
+            self.assertTrue(
+                torch.equal(
+                    torch.sort(X_pruned, stable=True).values,
+                    torch.sort(X[:2], stable=True).values,
+                )
+            )
             # test that zero-probability is in fact pruned
             samples[2, 0, 0] = 10
             with mock.patch.object(MockPosterior, "rsample", return_value=samples):
@@ -289,11 +290,7 @@ def test_prune_inferior_points(self):
                 device=self.device,
                 dtype=dtype,
             )
-            mm = MockModel(
-                MockPosterior(
-                    samples=samples,
-                )
-            )
+            mm = MockModel(MockPosterior(samples=samples))
             X_pruned = prune_inferior_points(
                 model=mm,
                 X=X,