diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py
index 3d9caeec5897fcd5b9e084aff496d150efee2066..919c531d184b0a95ce8b456d57465b90eee5003e 100644
--- a/python/paddle/v2/layer.py
+++ b/python/paddle/v2/layer.py
@@ -360,7 +360,7 @@ mixed.__doc__ = conf_helps.mixed_layer.__doc__
 
 
 class RecurrentLayerInput(Layer):
-    def __init__(self, recurrent_name, index, parent_layers):
+    def __init__(self, recurrent_name, index, parent_layers, reverse):
         parents_len = len(parent_layers)
         assert parents_len <= 1
         if parents_len == 0:
@@ -368,6 +368,7 @@ class RecurrentLayerInput(Layer):
         else:
             self.__parents__ = parent_layers.values()[0]
         self.__recurrent_name__ = recurrent_name
+        self.__reverse__ = reverse
         name = self.__parents__[
             index].name if index >= 0 else self.context_name()
         super(RecurrentLayerInput, self).__init__(
@@ -380,7 +381,8 @@ class RecurrentLayerInput(Layer):
         model_type('recurrent_nn')
         RecurrentLayerGroupWithoutOutLinksBegin(
             name=self.__recurrent_name__,
-            in_links=map(lambda x: x.name, self.__parents__))
+            in_links=map(lambda x: x.name, self.__parents__),
+            seq_reversed=self.__reverse__)
         return self
 
 
@@ -461,7 +463,7 @@ del each_layer_name
 
 
 @wrap_name_default()
-def recurrent_group(step, input, name=None):
+def recurrent_group(step, input, reverse=False, name=None):
     if not isinstance(input, collections.Sequence):
         input = [input]
 
@@ -471,14 +473,14 @@ def recurrent_group(step, input, name=None):
         RecurrentLayerInput(
             recurrent_name=name,
             index=i,
-            parent_layers={'recurrent_inputs': non_static_inputs})
-        for i in xrange(len(non_static_inputs))
+            parent_layers={'recurrent_inputs': non_static_inputs},
+            reverse=reverse) for i in xrange(len(non_static_inputs))
     ]
 
     extra_input = None
     if len(non_static_inputs) == 0:
         extra_input = RecurrentLayerInput(
-            recurrent_name=name, index=-1, parent_layers={})
+            recurrent_name=name, index=-1, parent_layers={}, reverse=reverse)
 
     def __real_step__(*args):
         rnn_input = list(args)
diff --git a/python/paddle/v2/tests/test_rnn_layer.py b/python/paddle/v2/tests/test_rnn_layer.py
index 5fbbd20eb76bb9daab2bcf98c4adad989106a377..845277c01288f99f75a148ddab5895d00864f60c 100644
--- a/python/paddle/v2/tests/test_rnn_layer.py
+++ b/python/paddle/v2/tests/test_rnn_layer.py
@@ -42,7 +42,8 @@ class RNNTest(unittest.TestCase):
             def test():
                 data = conf_helps.data_layer(name="word", size=dict_dim)
                 embd = conf_helps.embedding_layer(input=data, size=word_dim)
-                conf_helps.recurrent_group(name="rnn", step=step, input=embd)
+                conf_helps.recurrent_group(
+                    name="rnn", step=step, input=embd, reverse=True)
 
             return str(parse_network(test))
 
@@ -60,7 +61,7 @@ class RNNTest(unittest.TestCase):
                 name="word", type=data_type.integer_value(dict_dim))
             embd = layer.embedding(input=data, size=word_dim)
             rnn_layer = layer.recurrent_group(
-                name="rnn", step=new_step, input=embd)
+                name="rnn", step=new_step, input=embd, reverse=True)
             return str(layer.parse_network(rnn_layer))
 
         diff = difflib.unified_diff(parse_old_rnn().splitlines(1),