diff --git a/tests/gpu_tests/ddp_test.py b/tests/gpu_tests/ddp_test.py
index b500c67..f042a7f 100644
--- a/tests/gpu_tests/ddp_test.py
+++ b/tests/gpu_tests/ddp_test.py
@@ -15,8 +15,8 @@
     LAMBDA_FACTOR_NAMES,
 )
 from tests.gpu_tests.pipeline import (
-    ClassificationTask,
-    construct_mnist_mlp,
+    GpuTestTask,
+    construct_test_mlp,
     get_mnist_dataset,
 )
 from tests.gpu_tests.prepare_tests import QUERY_INDICES, TRAIN_INDICES
@@ -35,7 +35,7 @@
 class DDPTest(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
-        cls.model = construct_mnist_mlp()
+        cls.model = construct_test_mlp()
         cls.model.load_state_dict(torch.load("model.pth"))
         cls.model = cls.model.double()
 
@@ -44,7 +44,7 @@ def setUpClass(cls) -> None:
         cls.eval_dataset = get_mnist_dataset(split="valid", data_path="data")
         cls.eval_dataset = data.Subset(cls.eval_dataset, indices=list(range(QUERY_INDICES)))
 
-        cls.task = ClassificationTask()
+        cls.task = GpuTestTask()
         cls.model = prepare_model(cls.model, cls.task)
 
         dist.init_process_group("nccl", rank=WORLD_RANK, world_size=WORLD_SIZE)
@@ -187,6 +187,41 @@ def test_self_scores(self) -> None:
                 rtol=1e-3,
             )
 
+    def test_lr_pairwise_scores(self) -> None:
+        pairwise_scores = self.analyzer.load_pairwise_scores(scores_name="single_gpu_qb")
+
+        score_args = ScoreArguments(
+            score_dtype=torch.float64,
+            per_sample_gradient_dtype=torch.float64,
+            precondition_dtype=torch.float64,
+            query_gradient_rank=32
+        )
+        self.analyzer.compute_pairwise_scores(
+            scores_name="ddp_qb",
+            factors_name=OLD_FACTOR_NAME,
+            query_dataset=self.eval_dataset,
+            train_dataset=self.train_dataset,
+            train_indices=list(range(TRAIN_INDICES)),
+            query_indices=list(range(QUERY_INDICES)),
+            per_device_query_batch_size=12,
+            per_device_train_batch_size=512,
+            score_args=score_args,
+            overwrite_output_dir=True,
+        )
+        new_pairwise_scores = self.analyzer.load_pairwise_scores(scores_name=NEW_SCORE_NAME)
+
+        if LOCAL_RANK == 0:
+            print(f"Previous score: {pairwise_scores[ALL_MODULE_NAME][0]}")
+            print(f"Previous shape: {pairwise_scores[ALL_MODULE_NAME].shape}")
+            print(f"New score: {new_pairwise_scores[ALL_MODULE_NAME][0]}")
+            print(f"New shape: {new_pairwise_scores[ALL_MODULE_NAME].shape}")
+            assert check_tensor_dict_equivalence(
+                pairwise_scores,
+                new_pairwise_scores,
+                atol=1e-5,
+                rtol=1e-3,
+            )
+
     @classmethod
     def tearDownClass(cls) -> None:
         dist.destroy_process_group()