diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py b/egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py
index 6f7231f4b..e2ec2e1cf 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py
@@ -87,10 +87,17 @@ class Conformer(EncoderInterface):
             layer_dropout,
             cnn_module_kernel,
         )
+        # aux_layers from 1/3
         self.encoder = ConformerEncoder(
             encoder_layer,
             num_encoder_layers,
-            aux_layers=list(range(0, num_encoder_layers - 1, aux_layer_period)),
+            aux_layers=list(
+                range(
+                    num_encoder_layers // 3,
+                    num_encoder_layers - 1,
+                    aux_layer_period,
+                )
+            ),
         )
 
     def forward(
@@ -296,10 +303,10 @@ class ConformerEncoder(nn.Module):
         assert num_layers - 1 not in aux_layers
         self.aux_layers = set(aux_layers + [num_layers - 1])
 
-        num_channels = encoder_layer.norm_final.num_channels
+        # num_channels = encoder_layer.norm_final.num_channels
         self.combiner = RandomCombine(
             num_inputs=len(self.aux_layers),
-            num_channels=num_channels,
+            # num_channels=num_channels,
             final_weight=0.5,
             pure_prob=0.333,
             stddev=2.0,
@@ -1073,7 +1080,7 @@ class RandomCombine(nn.Module):
     def __init__(
         self,
         num_inputs: int,
-        num_channels: int,
+        # num_channels: int,
         final_weight: float = 0.5,
         pure_prob: float = 0.5,
         stddev: float = 2.0,
@@ -1116,12 +1123,12 @@ class RandomCombine(nn.Module):
         assert 0 < final_weight < 1, final_weight
         assert num_inputs >= 1
 
-        self.linear = nn.ModuleList(
-            [
-                nn.Linear(num_channels, num_channels, bias=True)
-                for _ in range(num_inputs - 1)
-            ]
-        )
+        # self.linear = nn.ModuleList(
+        #     [
+        #         nn.Linear(num_channels, num_channels, bias=True)
+        #         for _ in range(num_inputs - 1)
+        #     ]
+        # )
 
         self.num_inputs = num_inputs
         self.final_weight = final_weight
@@ -1135,12 +1142,13 @@ class RandomCombine(nn.Module):
             .log()
             .item()
         )
-        self._reset_parameters()
 
-    def _reset_parameters(self):
-        for i in range(len(self.linear)):
-            nn.init.eye_(self.linear[i].weight)
-            nn.init.constant_(self.linear[i].bias, 0.0)
+    #     self._reset_parameters()
+
+    # def _reset_parameters(self):
+    #     for i in range(len(self.linear)):
+    #         nn.init.eye_(self.linear[i].weight)
+    #         nn.init.constant_(self.linear[i].bias, 0.0)
 
     def forward(self, inputs: List[Tensor]) -> Tensor:
         """Forward function.
@@ -1163,7 +1171,8 @@ class RandomCombine(nn.Module):
 
         mod_inputs = []
         for i in range(num_inputs - 1):
-            mod_inputs.append(self.linear[i](inputs[i]))
+            # mod_inputs.append(self.linear[i](inputs[i]))
+            mod_inputs.append(inputs[i])
         mod_inputs.append(inputs[num_inputs - 1])
 
         ndim = inputs[0].ndim