diff --git a/go/pserver/client/c/test/test_train.py b/go/pserver/client/c/test/test_train.py
index e9264592b4f18fddf68b198d73bf907206e77a3f..17082cf8926deb1ebd3b20e2e37c3895aee30b81 100644
--- a/go/pserver/client/c/test/test_train.py
+++ b/go/pserver/client/c/test/test_train.py
@@ -38,7 +38,7 @@ def main():
     parameters = paddle.parameters.create(cost)
 
     # create optimizer of new remote updater to pserver
-    optimizer = paddle.optimizer.Momentum(momentum=0)
+    optimizer = paddle.optimizer.Momentum(momentum=0, learning_rate=1e-3)
 
     print "etcd endoint: ", etcd_endpoint
     trainer = paddle.trainer.SGD(cost=cost,
diff --git a/paddle/trainer/NewRemoteParameterUpdater.cpp b/paddle/trainer/NewRemoteParameterUpdater.cpp
index a830ceba5772846cd9255a3eeb26e8d6a17dcfbc..e1558e3fdfbcf296be0ee64202132f53bf901be9 100644
--- a/paddle/trainer/NewRemoteParameterUpdater.cpp
+++ b/paddle/trainer/NewRemoteParameterUpdater.cpp
@@ -76,7 +76,11 @@ void NewRemoteParameterUpdater::init(
       sgdConfigV2->set_decay(paramConfig.decay_rate());
       optimizeConfigV2.set_lr_policy(paddle::OptimizerConfig::Const);
       auto constlr = optimizeConfigV2.mutable_const_lr();
-      constlr->set_learning_rate(paramConfig.learning_rate());
+      if (paramConfig.has_learning_rate()) {
+        constlr->set_learning_rate(paramConfig.learning_rate());
+      } else {
+        constlr->set_learning_rate(trainerConfig_.learning_rate());
+      }
       if (trainerConfig_.algorithm() == "sgd") {
         optimizeConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
         // FIXME: config all algorithms