diff --git a/external/model-preparation-algorithm/mpa_tasks/apis/classification/task.py b/external/model-preparation-algorithm/mpa_tasks/apis/classification/task.py
index e366790f88f..88b288c69ef 100644
--- a/external/model-preparation-algorithm/mpa_tasks/apis/classification/task.py
+++ b/external/model-preparation-algorithm/mpa_tasks/apis/classification/task.py
@@ -368,8 +368,6 @@ def patch_color_conversion(pipeline):
             elif self._hierarchical:
                 cfg.type = "MPAHierarchicalClsDataset"
                 cfg.hierarchical_info = self._hierarchical_info
-                if subset == "train":
-                    cfg.drop_last = True  # For stable hierarchical information indexing
             else:
                 cfg.type = "MPAClsDataset"
 
diff --git a/external/model-preparation-algorithm/submodule b/external/model-preparation-algorithm/submodule
index 66d4e3ad1ec..726f545bc3d 160000
--- a/external/model-preparation-algorithm/submodule
+++ b/external/model-preparation-algorithm/submodule
@@ -1 +1 @@
-Subproject commit 66d4e3ad1ecb2ea82023e82f1634baa996a5fff5
+Subproject commit 726f545bc3d3d35172fd70a2b4f7c7546b04f52d
diff --git a/ote_cli/ote_cli/utils/tests.py b/ote_cli/ote_cli/utils/tests.py
index 264d81dead4..cacd4c5cea2 100644
--- a/ote_cli/ote_cli/utils/tests.py
+++ b/ote_cli/ote_cli/utils/tests.py
@@ -213,7 +213,8 @@ def ote_eval_openvino_testing(template, root, ote_dir, args, threshold):
 
     for k in trained_performance.keys():
         assert (
-            abs(trained_performance[k] - exported_performance[k])
+            exported_performance[k] > trained_performance[k]
+            or abs(trained_performance[k] - exported_performance[k])
             / (trained_performance[k] + 1e-10)
             <= threshold
         ), f"{trained_performance[k]=}, {exported_performance[k]=}"
@@ -520,7 +521,8 @@ def nncf_eval_testing(template, root, ote_dir, args, threshold):
 
     for k in trained_performance.keys():
         assert (
-            abs(trained_performance[k] - evaluated_performance[k])
+            evaluated_performance[k] > trained_performance[k]
+            or abs(trained_performance[k] - evaluated_performance[k])
             / (trained_performance[k] + 1e-10)
             <= threshold
         ), f"{trained_performance[k]=}, {evaluated_performance[k]=}"