diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index 796121a64136ee3f31b2ed09b761c6a83cdbe625..952b1f097133c68643cb201fa30452e9e08afcd8 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -2754,7 +2754,12 @@ class SubsequenceInput(object):
 
 
 @wrap_name_default("recurrent_group")
-def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
+def recurrent_group(step,
+                    input,
+                    reverse=False,
+                    name=None,
+                    targetInlink=None,
+                    is_train=True):
     """
     Recurrent layer group is an extremely flexible recurrent unit in
     PaddlePaddle. As long as the user defines the calculation done within a
@@ -2819,6 +2824,12 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
 
     :type targetInlink: LayerOutput|SubsequenceInput
 
+    :param is_train: recurrent_group is used for training (True) or generating (False).
+                     If is training, one of the input type must be LayerOutput; else, 
+                     none of input type should be LayerOutput.
+
+    : type is_train: bool                 
+    
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -2866,6 +2877,7 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
         seq_reversed=reverse,
         target_inlinkname=targetInlinkName)
     in_args = []
+    has_LayerOutput = True
     for each_input in input:
         assert is_single_input(each_input)
         if isinstance(each_input, LayerOutput):
@@ -2873,6 +2885,7 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
         elif isinstance(each_input, SubsequenceInput):
             in_args.append(each_input.input)
         else:
+            has_LayerOutput = False
             mem_name = "__%s_memory__" % each_input.input.name
             mem = memory(
                 name=mem_name,
@@ -2886,6 +2899,8 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
                 mix += identity_projection(mem)
             in_args.append(mem)
 
+    assert (is_train == has_LayerOutput)
+
     layer_outs = step(*in_args)
 
     if isinstance(layer_outs, LayerOutput):
@@ -3177,7 +3192,11 @@ def beam_search(step,
         return predict
 
     tmp = recurrent_group(
-        step=__real_step__, input=real_input, reverse=False, name=name)
+        step=__real_step__,
+        input=real_input,
+        reverse=False,
+        name=name,
+        is_train=False)
 
     return tmp