diff --git a/examples/benchmarks/CatBoost/workflow_config_catboost.yaml b/examples/benchmarks/CatBoost/workflow_config_catboost.yaml
index 574b52ddd..720599be5 100644
--- a/examples/benchmarks/CatBoost/workflow_config_catboost.yaml
+++ b/examples/benchmarks/CatBoost/workflow_config_catboost.yaml
@@ -32,7 +32,11 @@ task:
             loss: RMSE
             learning_rate: 0.0421
             subsample: 0.8789
+            max_depth: 6
+            num_leaves: 100
             thread_count: 20
+            grow_policy: Lossguide
+            boostrap_type: Poisson
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset
diff --git a/examples/benchmarks/TFT/tft.py b/examples/benchmarks/TFT/tft.py
index 92d957d0f..a3b4fc919 100644
--- a/examples/benchmarks/TFT/tft.py
+++ b/examples/benchmarks/TFT/tft.py
@@ -232,7 +232,7 @@ class TFTModel(ModelFT):
             p90_forecast = self.data_formatter.format_predictions(output_map["p90"])
             tf.keras.backend.set_session(default_keras_session)
 
-        predict = format_score(p90_forecast, "pred", 0) # self.label_shift
+        predict = format_score(p90_forecast, "pred", 0)  # self.label_shift
         label = format_score(targets, "label", 0)
         # ===========================Predicting Process===========================
         return predict, label
diff --git a/examples/run_all_model.py b/examples/run_all_model.py
index f8894afd3..b448a1857 100644
--- a/examples/run_all_model.py
+++ b/examples/run_all_model.py
@@ -178,8 +178,7 @@ def get_all_folders() -> dict:
     folders = dict()
     for f in os.scandir("benchmarks"):
         path = Path("benchmarks") / f.name
-        if f.name != "TFT":
-            folders[f.name] = str(path.resolve())
+        folders[f.name] = str(path.resolve())
     return folders
 
 
diff --git a/qlib/data/dataset/loader.py b/qlib/data/dataset/loader.py
index 404313e80..db6b1440d 100644
--- a/qlib/data/dataset/loader.py
+++ b/qlib/data/dataset/loader.py
@@ -21,18 +21,16 @@ class DataLoader(abc.ABC):
     @abc.abstractmethod
     def load(self, instruments, start_time=None, end_time=None) -> pd.DataFrame:
         """
-        load the data as pd.DataFrame
+        load the data as pd.DataFrame.
 
         Parameters
         ----------
-        self : [TODO:type]
-            [TODO:description]
-        instruments : [TODO:type]
-            [TODO:description]
-        start_time : [TODO:type]
-            [TODO:description]
-        end_time : [TODO:type]
-            [TODO:description]
+        instruments : str or dict
+            it can either be the market name or the config file of instruments generated by InstrumentProvider.
+        start_time : str
+            start of the time range.
+        end_time : str
+            end of the time range.
 
         Returns
         -------