diff --git a/qlib/data/data.py b/qlib/data/data.py
index 809b8d1c32..1a6f3ee6ed 100644
--- a/qlib/data/data.py
+++ b/qlib/data/data.py
@@ -587,7 +587,10 @@ def dataset_processor(instruments_d, column_names, start_time, end_time, freq, i
 
         if len(new_data) > 0:
             data = pd.concat(new_data, names=["instrument"], sort=False)
-            data = DiskDatasetCache.cache_to_origin_data(data, column_names)
+
+            # NOTE: InstProcessors may add new columns and using cache_to_origin_data will remove those added columns.
+            if not len(inst_processors):
+                data = DiskDatasetCache.cache_to_origin_data(data, column_names)
         else:
             data = pd.DataFrame(
                 index=pd.MultiIndex.from_arrays([[], []], names=("instrument", "datetime")),
diff --git a/qlib/data/dataset/loader.py b/qlib/data/dataset/loader.py
index e9d6f98866..3d0fd95465 100644
--- a/qlib/data/dataset/loader.py
+++ b/qlib/data/dataset/loader.py
@@ -10,7 +10,13 @@
 from typing import Tuple, Union, List
 
 from qlib.data import D
-from qlib.utils import load_dataset, init_instance_by_config, time_to_slc_point
+from qlib.utils import (
+    load_dataset,
+    init_instance_by_config,
+    time_to_slc_point,
+    remove_fields_space,
+    remove_repeat_field,
+)
 from qlib.log import get_module_logger
 from qlib.utils.serial import Serializable
 
@@ -215,7 +221,12 @@ def load_group_df(
             self.inst_processors if isinstance(self.inst_processors, list) else self.inst_processors.get(gp_name, [])
         )
         df = D.features(instruments, exprs, start_time, end_time, freq=freq, inst_processors=inst_processors)
-        df.columns = names
+        # NOTE: InstProcessors may add new columns
+        if len(inst_processors):
+            df.rename(columns=dict(zip(remove_repeat_field(remove_fields_space(exprs)), names)), inplace=True)
+        else:
+            df.columns = names
+
         if self.swap_level:
             df = df.swaplevel().sort_index()  # NOTE: if swaplevel, return <datetime, instrument>
         return df