diff --git a/example/extensions/lib_pass/test_pass.py b/example/extensions/lib_pass/test_pass.py
index 8930c9478152..5d4578391097 100644
--- a/example/extensions/lib_pass/test_pass.py
+++ b/example/extensions/lib_pass/test_pass.py
@@ -48,10 +48,11 @@
 sym = mx.sym.log(d)
 
 def test_model(pass_name):
+    args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))}
     # execute in MXNet
     print('-------------------------------')
     print('Testing regular MXNet execution')
-    exe = sym.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+    exe = sym.bind(ctx=mx.cpu(), args=args)
     out = exe.forward()
     print(out)
 
@@ -59,11 +60,10 @@ def test_model(pass_name):
     # with propogating shapes/types
     print('-------------------------------')
     print('Testing pass "%s" with shapes/types' % pass_name)
-    arg_array = [mx.nd.ones((3,2),dtype='float32'), mx.nd.ones((3,2),dtype='float32')]
-    aux = []
-    mysym2 = sym.optimize_for(pass_name,arg_array,aux)
+    aux = {}
+    mysym2 = sym.optimize_for(pass_name,args,aux)
     print(mysym2.tojson())
-    exe2 = mysym2.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+    exe2 = mysym2.bind(ctx=mx.cpu(), args=args)
     out2 = exe2.forward()
     print(out2)
 
@@ -71,7 +71,7 @@ def test_model(pass_name):
     print('-------------------------------')
     print('Testing pass "%s" without shapes/types' % pass_name)
     mysym3 = sym.optimize_for(pass_name, myOpt='yello')
-    exe3 = mysym3.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+    exe3 = mysym3.bind(ctx=mx.cpu(), args=args)
     out3 = exe3.forward()
     print(out3)
 
diff --git a/example/extensions/lib_subgraph/test_subgraph.py b/example/extensions/lib_subgraph/test_subgraph.py
index eb7102a1511c..fa56b50515e5 100644
--- a/example/extensions/lib_subgraph/test_subgraph.py
+++ b/example/extensions/lib_subgraph/test_subgraph.py
@@ -49,32 +49,31 @@
 sym2 = mx.sym.log(d2)
 
 def test(backend):
+    args = {'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))}
     ###############################################
     # Test with subgraph not consuming params
     ###############################################
     #execute in MXNet
     print('-------------------------------')
     print('Testing regular MXNet execution')
-    exe = sym.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+    exe = sym.bind(ctx=mx.cpu(), args=args)
     out = exe.forward()
     print(out)
 
     # with propogating shapes/types
     print('-------------------------------')
     print('Testing %s partitioning with shapes/types' % backend)
-    arg_array = [mx.nd.ones((3,2),dtype='float32'), mx.nd.ones((3,2),dtype='float32')]
-    mysym2 = sym.optimize_for(backend,arg_array)
+    mysym2 = sym.optimize_for(backend,args)
     print(mysym2.tojson())
-    exe2 = mysym2.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+    exe2 = mysym2.bind(ctx=mx.cpu(), args=args)
     out2 = exe2.forward()
     print(out2)
 
     # with propogating shapes/types, rejecting subgraph
     print('-------------------------------')
     print('Testing %s partitioning with shapes/types - rejecting subgraph' % backend)
-    arg_array = [mx.nd.ones((3,2),dtype='float32'), mx.nd.ones((3,2),dtype='float32')]
-    mysym2 = sym.optimize_for(backend, arg_array, reject=True)
-    exe2 = mysym2.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+    mysym2 = sym.optimize_for(backend, args, reject=True)
+    exe2 = mysym2.bind(ctx=mx.cpu(), args=args)
     out2 = exe2.forward()
     print(out2)
 
@@ -82,7 +81,7 @@ def test(backend):
     print('-------------------------------')
     print('Testing %s partitioning without shapes/types' % backend)
     mysym3 = sym.optimize_for(backend, myOpt='yello')
-    exe3 = mysym3.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2)), 'b':mx.nd.ones((3,2))})
+    exe3 = mysym3.bind(ctx=mx.cpu(), args=args)
     out3 = exe3.forward()
     print(out3)
 
@@ -108,20 +107,20 @@ def test(backend):
     ###############################################
     # Test with subgraph directly consuming params
     ###############################################
+    args = {'a':mx.nd.ones((3,2))}
     #execute in MXNet
     print('-------------------------------')
     print('Testing regular MXNet execution')
-    exe5 = sym2.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2))})
+    exe5 = sym2.bind(ctx=mx.cpu(), args=args)
     out5 = exe5.forward()
     print(out5)
 
     # with propogating shapes/types
     print('-------------------------------')
     print('Testing %s partitioning with shapes/types' % backend)
-    arg_array = [mx.nd.ones((3,2),dtype='float32')]
-    mysym6 = sym2.optimize_for(backend, arg_array, reqArgs=True)
+    mysym6 = sym2.optimize_for(backend, args, reqArgs=True)
     print(mysym6.tojson())
-    exe6 = mysym6.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2))})
+    exe6 = mysym6.bind(ctx=mx.cpu(), args=args)
     out6 = exe6.forward()
     print(out6)
 
@@ -129,7 +128,7 @@ def test(backend):
     print('-------------------------------')
     print('Testing %s partitioning without shapes/types' % backend)
     mysym7 = sym2.optimize_for(backend, reqArgs=True)
-    exe7 = mysym7.bind(ctx=mx.cpu(), args={'a':mx.nd.ones((3,2))})
+    exe7 = mysym7.bind(ctx=mx.cpu(), args=args)
     out7 = exe7.forward()
     print(out7)
 
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index 78b5a7262121..6c3612c7d784 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -1033,12 +1033,12 @@ def _build_cache(self, *args):
         if self._backend:
             ctx = args[0].context
             # get list of params in the order of out.list_arguments
-            arg_array = [args[data_names[name]] if name in data_names.keys() else params[name].data()
-                         for name in out.list_arguments()]
-            aux_array = [args[data_names[name]] if name in data_names.keys() else params[name].data()
-                         for name in out.list_auxiliary_states()]
+            arg_dict = {name:args[data_names[name]] if name in data_names.keys() else params[name].data()
+                        for name in out.list_arguments()}
+            aux_dict = {name:args[data_names[name]] if name in data_names.keys() else params[name].data()
+                        for name in out.list_auxiliary_states()}
             # Partition the graph.
-            out = out.optimize_for(self._backend, arg_array, aux_array, ctx, **self._backend_opts)
+            out = out.optimize_for(self._backend, arg_dict, aux_dict, ctx, **self._backend_opts)
             #update cached graph with partitioned graph
             self._cached_graph = data, out
         self._cached_op = ndarray.CachedOp(out, flags)
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index d4ff7954c181..5fb1b8c4e700 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -1456,17 +1456,15 @@ def optimize_for(self, backend, args=None, aux=None, ctx=None, **kwargs):
         backend : str
             The name of backend, as registered in `SubgraphBackendRegistry`
 
-        args : list of NDArray or dict of str to NDArray, optional
+        args : dict of str to NDArray, optional
             Input arguments to the symbol, required to infer shapes/types before partitioning
 
-            - If type is a list of `NDArray`, the order is the same as that of `list_arguments()`.
             - If type is a dict of str to `NDArray`, then it maps the name of arguments
               to the corresponding `NDArray`.
 
-        aux : list of NDArray or dict of str to NDArray, optional
+        aux : dict of str to NDArray, optional
             Input auxiliary arguments to the symbol
 
-            - If type is a list of `NDArray`, the order is the same as that of `list_arguments()`.
             - If type is a dict of str to `NDArray`, then it maps the name of arguments
               to the corresponding `NDArray`.
 
@@ -1483,6 +1481,8 @@ def optimize_for(self, backend, args=None, aux=None, ctx=None, **kwargs):
         """
         out = SymbolHandle()
         assert isinstance(backend, str)
+        assert isinstance(args, dict) or args is None
+        assert isinstance(aux, dict) or aux is None
 
         if args is None or len(args) == 0:
             args_ = []
@@ -1530,30 +1530,22 @@ def optimize_for(self, backend, args=None, aux=None, ctx=None, **kwargs):
                                              ctypes.byref(new_aux_size),
                                              ctypes.byref(new_aux_handle),
                                              ctypes.byref(new_aux_names)))
-        arg_names = self.list_arguments()
-        if isinstance(args, dict):
+        # add new args/aux
+        if not args is None:
             for i in range(new_args_size.value):
                 args[py_str(new_arg_names[i])] = NDArray(NDArrayHandle(new_args_handle[i]))
-        elif isinstance(args, list):
-            for i in range(new_args_size.value):
-                name = py_str(new_arg_names[i])
-                if name in arg_names:
-                    idx = arg_names.index(name)
-                    args[idx] = NDArray(NDArrayHandle(new_args_handle[i]))
-                else:
-                    args.append(NDArray(NDArrayHandle(new_args_handle[i])))
-        aux_names = self.list_auxiliary_states()
-        if isinstance(aux, dict):
+        elif new_args_size.value > 0:
+            raise RuntimeError('Cannot add new args in optimize_for since args is None\n' +
+                               'Provide a dictionary to the args argument to optimize_for')
+
+        if not aux is None:
             for i in range(new_aux_size.value):
                 aux[py_str(new_aux_names[i])] = NDArray(NDArrayHandle(new_aux_handle[i]))
-        elif isinstance(aux, list):
-            for i in range(new_aux_size.value):
-                name = py_str(new_aux_names[i])
-                if name in aux_names:
-                    idx = aux_names.index(name)
-                    aux[idx] = NDArray(NDArrayHandle(new_aux_handle[i]))
-                else:
-                    aux.append(NDArray(NDArrayHandle(new_aux_handle[i])))
+        elif new_aux_size.value > 0:
+            raise RuntimeError('Cannot add new aux in optimize_for since aux is None\n' +
+                               'Provide a dictionary to the aux argument to optimize_for')
+
+        # return modified symbol
         return Symbol(out)
 
 
diff --git a/tests/python/unittest/test_extensions.py b/tests/python/unittest/test_extensions.py
index d60ca0d352a2..2e21f927737b 100644
--- a/tests/python/unittest/test_extensions.py
+++ b/tests/python/unittest/test_extensions.py
@@ -130,8 +130,6 @@ def test_subgraph():
     sym = mx.sym.log(d)
 
     args = {'a':mx.nd.ones((3,2),ctx=mx.cpu()), 'b':mx.nd.ones((3,2),ctx=mx.cpu())}
-    arg_array = [mx.nd.ones((3,2),dtype='float32',ctx=mx.cpu()),
-                 mx.nd.ones((3,2),dtype='float32',ctx=mx.cpu())]
 
     # baseline - regular execution in MXNet
     exe = sym.bind(ctx=mx.cpu(), args=args)
@@ -147,14 +145,14 @@ def test_subgraph():
 
     # with propogating shapes/types, rejecting subgraph
     # this tests creating the subgraph and having the subgraph prop reject it
-    mysym2 = sym.optimize_for("myProp", arg_array, reject=True)
+    mysym2 = sym.optimize_for("myProp", args, reject=True)
     exe2 = mysym2.bind(ctx=mx.cpu(), args=args)
     out2 = exe2.forward()
     # check that result matches one executed by MXNet
     assert_almost_equal(out[0].asnumpy(), out2[0].asnumpy(), rtol=1e-3, atol=1e-3)
 
     # with propogating shapes/types
-    mysym3 = sym.optimize_for("myProp",arg_array)
+    mysym3 = sym.optimize_for("myProp",args)
     exe3 = mysym3.bind(ctx=mx.cpu(), args=args)
     out3 = exe3.forward()
     # check that result matches one executed by MXNet
diff --git a/tests/python/unittest/test_subgraph_op.py b/tests/python/unittest/test_subgraph_op.py
index f5b96d84aefe..d27295e584e0 100644
--- a/tests/python/unittest/test_subgraph_op.py
+++ b/tests/python/unittest/test_subgraph_op.py
@@ -353,18 +353,20 @@ def test_subgraph_exe8(sym, subgraph_backend, op_names):
     # bind
     sym, _, _ = sym
     arg_shapes, _, aux_shapes = sym.infer_shape()
-    arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
-    aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
-    exe1 = sym.bind(ctx=mx.current_context(), args=arg_array, aux_states=aux_array, grad_req='null')
+    arg_names = sym.list_arguments()
+    aux_names = sym.list_auxiliary_states()
+    arg_dict = {name:mx.nd.random.uniform(shape=shape) for name,shape in zip(arg_names,arg_shapes)}
+    aux_dict = {name:mx.nd.random.uniform(shape=shape) for name,shape in zip(aux_names,aux_shapes)}
+    exe1 = sym.bind(ctx=mx.current_context(), args=arg_dict, aux_states=aux_dict, grad_req='null')
     exe1.forward()
 
     # infer shape/type before partition before bind
     check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend), mx_uint(len(op_names)),
-                                                 c_str_array(op_names)))
-    part_sym = sym.optimize_for(subgraph_backend, arg_array, aux_array)
+                                                   c_str_array(op_names)))
+    part_sym = sym.optimize_for(subgraph_backend, arg_dict, aux_dict)
     check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
 
-    exe2 = part_sym.bind(ctx=mx.current_context(), args=arg_array, aux_states=aux_array, grad_req='null')
+    exe2 = part_sym.bind(ctx=mx.current_context(), args=arg_dict, aux_states=aux_dict, grad_req='null')
     exe2.forward()
 
     # compare outputs