diff --git a/examples/example_m5/main.py b/examples/example_m5/main.py
index a19b14a..7d4f979 100644
--- a/examples/example_m5/main.py
+++ b/examples/example_m5/main.py
@@ -124,7 +124,7 @@ class M5DatasetWorkflow:
         m5 = DataLoader()
         idx_list = m5.get_idx_list()
         os.makedirs("./user_spec", exist_ok=True)
-        sinle_score_list = []
+        single_score_list = []
         random_score_list = []
         job_selector_score_list = []
         ensemble_score_list = []
@@ -154,7 +154,7 @@ class M5DatasetWorkflow:
                 pred_y = learnware.predict(test_x)
                 loss_list.append(m5.score(test_y, pred_y))
             print(
-                f"Top1-score: {sorted_score_list[0]}, learnware_id: {single_learnware_list[0].id}, loss: {loss_list[-1]}"
+                f"Top1-score: {sorted_score_list[0]}, learnware_id: {single_learnware_list[0].id}, loss: {loss_list[0]}"
             )
 
             mixture_id = " ".join([learnware.id for learnware in mixture_learnware_list])
@@ -170,12 +170,12 @@ class M5DatasetWorkflow:
             ensemble_score = m5.score(test_y, ensemble_predict_y)
             print(f"mixture reuse loss (ensemble): {ensemble_score}\n")
 
-            sinle_score_list.append(loss_list[0])
+            single_score_list.append(loss_list[0])
             random_score_list.append(np.mean(loss_list))
             job_selector_score_list.append(job_selector_score)
             ensemble_score_list.append(ensemble_score)
 
-        print(f"Single search score: {np.mean(sinle_score_list)}")
+        print(f"Single search score: {np.mean(single_score_list)}")
         print(f"Job selector score: {np.mean(job_selector_score_list)}")
         print(f"Average ensemble score: {np.mean(ensemble_score_list)}")
         print(f"Random search score: {np.mean(random_score_list)}")
diff --git a/examples/example_pfs/main.py b/examples/example_pfs/main.py
index f6038dc..25da85b 100644
--- a/examples/example_pfs/main.py
+++ b/examples/example_pfs/main.py
@@ -122,7 +122,7 @@ class PFSDatasetWorkflow:
         pfs = Dataloader()
         idx_list = pfs.get_idx_list()
         os.makedirs("./user_spec", exist_ok=True)
-        sinle_score_list = []
+        single_score_list = []
         random_score_list = []
         job_selector_score_list = []
         ensemble_score_list = []
@@ -152,7 +152,7 @@ class PFSDatasetWorkflow:
                 pred_y = learnware.predict(test_x)
                 loss_list.append(pfs.score(test_y, pred_y))
             print(
-                f"Top1-score: {sorted_score_list[0]}, learnware_id: {single_learnware_list[0].id}, loss: {loss_list[-1]}"
+                f"Top1-score: {sorted_score_list[0]}, learnware_id: {single_learnware_list[0].id}, loss: {loss_list[0]}"
             )
 
             mixture_id = " ".join([learnware.id for learnware in mixture_learnware_list])
@@ -168,12 +168,12 @@ class PFSDatasetWorkflow:
             ensemble_score = pfs.score(test_y, ensemble_predict_y)
             print(f"mixture reuse loss (ensemble): {ensemble_score}\n")
 
-            sinle_score_list.append(loss_list[0])
+            single_score_list.append(loss_list[0])
             random_score_list.append(np.mean(loss_list))
             job_selector_score_list.append(job_selector_score)
             ensemble_score_list.append(ensemble_score)
 
-        print(f"Single search score: {np.mean(sinle_score_list)}")
+        print(f"Single search score: {np.mean(single_score_list)}")
         print(f"Job selector score: {np.mean(job_selector_score_list)}")
         print(f"Average ensemble score: {np.mean(ensemble_score_list)}")
         print(f"Random search score: {np.mean(random_score_list)}")
diff --git a/learnware/learnware/reuse.py b/learnware/learnware/reuse.py
index b4639a9..97ee44e 100644
--- a/learnware/learnware/reuse.py
+++ b/learnware/learnware/reuse.py
@@ -73,17 +73,18 @@ class JobSelectorReuser(BaseReuser):
                 for learnware in self.learnware_list
             ]
 
-            task_matrix = np.zeros((len(learnware_rkme_spec_list), len(learnware_rkme_spec_list)))
-            for i in range(len(self.learnware_list)):
-                task_rkme1 = learnware_rkme_spec_list[i]
-                task_matrix[i][i] = task_rkme1.inner_prod(task_rkme1)
-                for j in range(i + 1, len(self.learnware_list)):
-                    task_rkme2 = learnware_rkme_spec_list[j]
-                    task_matrix[i][j] = task_matrix[j][i] = task_rkme1.inner_prod(task_rkme2)
-
-            task_mixture_weight = self._calculate_rkme_spec_mixture_weight(
-                user_data, learnware_rkme_spec_list, task_matrix
-            )
+            if self.use_herding:
+                task_matrix = np.zeros((len(learnware_rkme_spec_list), len(learnware_rkme_spec_list)))
+                for i in range(len(self.learnware_list)):
+                    task_rkme1 = learnware_rkme_spec_list[i]
+                    task_matrix[i][i] = task_rkme1.inner_prod(task_rkme1)
+                    for j in range(i + 1, len(self.learnware_list)):
+                        task_rkme2 = learnware_rkme_spec_list[j]
+                        task_matrix[i][j] = task_matrix[j][i] = task_rkme1.inner_prod(task_rkme2)
+
+                task_mixture_weight = self._calculate_rkme_spec_mixture_weight(
+                    user_data, learnware_rkme_spec_list, task_matrix
+                )
 
             herding_X, train_herding_X, val_herding_X = None, None, None
             herding_y, train_herding_y, val_herding_y = [], [], []
@@ -275,7 +276,7 @@ class AveragingReuser(BaseReuser):
                 # print(pred_y.shape)
                 if not isinstance(pred_y, np.ndarray):
                     pred_y = pred_y.detach().cpu().numpy()
-                softmax_pred = softmax(pred_y, axis=1)
+                softmax_pred = softmax(pred_y, axis=0)
                 if mean_pred_y is None:
                     mean_pred_y = softmax_pred
                 else: