diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index 1edcb5a74a77..f22f263b6ee9 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -88,11 +88,11 @@ struct ReduceAxisParam : public dmlc::Parameter<ReduceAxisParam> {
   dmlc::optional<int> axis;
   bool keepdims;
   DMLC_DECLARE_PARAMETER(ReduceAxisParam) {
-    DMLC_DECLARE_FIELD(axis).set_default(dmlc::optional<int>())
+    DMLC_DECLARE_FIELD(axis).set_default(dmlc::optional<int>(-1))
       .describe("The axis along which to perform the reduction. "
                 "Negative values means indexing from right to left. "
-                "``Requires axis to be set as int, because global reduction "
-                "is not supported yet.``");
+                "``The axis need to be set as an int. If the axis is "
+                "not set, the rightmost axis will be reduced.``");
     DMLC_DECLARE_FIELD(keepdims).set_default(false)
       .describe("If this is set to `True`, the reduced axis is left "
                 "in the result as dimension with size one.");
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 0aa48553901b..897079dc39c8 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -477,7 +477,6 @@ def test_dot():
     C = mx.nd.dot(A, B, transpose_a=True, transpose_b=True)
     assert_almost_equal(c, C.asnumpy(), atol=atol)
 
-
 @with_seed()
 def test_reduce():
     sample_num = 200
@@ -524,6 +523,31 @@ def test_reduce_inner(numpy_reduce_func, nd_reduce_func, multi_axes):
                              keepdims:np_reduce(np.float32(data), axis, keepdims, np.argmin),
                       mx.nd.argmin, False)
 
+@with_seed()
+def test_argmax_argmin():
+    # test optional parameters
+    # test name : input data, argmax result, argmin result
+    tests = {
+        'axis_0' : [[[1, 2, 3], [4, 5, 6]], [1, 1, 1], [0, 0, 0]],
+        'keep_dims' : [[[1, 2, 3], [4, 5, 6]], [[2], [2]], [[0], [0]]],
+        'axis_none' : [[1, 2, 3, 4], 3, 0]
+    }
+
+    arg_max = mx.nd.array(tests['axis_0'][0]).argmax(axis=0)
+    arg_min = mx.nd.array(tests['axis_0'][0]).argmin(axis=0)
+    assert_almost_equal(arg_max.asnumpy(), tests['axis_0'][1])
+    assert_almost_equal(arg_min.asnumpy(), tests['axis_0'][2])
+
+    arg_max = mx.nd.array(tests['keep_dims'][0]).argmax(axis=1, keepdims=True)
+    arg_min = mx.nd.array(tests['keep_dims'][0]).argmin(axis=1, keepdims=True)
+    assert_almost_equal(arg_max.asnumpy(), tests['keep_dims'][1])
+    assert_almost_equal(arg_min.asnumpy(), tests['keep_dims'][2])
+
+    arg_max = mx.nd.array(tests['axis_none'][0]).argmax()
+    arg_min = mx.nd.array(tests['axis_none'][0]).argmin()
+    assert_almost_equal(arg_max.asnumpy(), tests['axis_none'][1])
+    assert_almost_equal(arg_min.asnumpy(), tests['axis_none'][2])
+
 @with_seed()
 def test_broadcast():
     sample_num = 1000