diff --git a/test/test_zero1.py b/test/test_zero1.py
index 13588d779348..5ed8d742c9fb 100644
--- a/test/test_zero1.py
+++ b/test/test_zero1.py
@@ -88,6 +88,54 @@ def test_zero1(self):
     opt2.step()
     xm.mark_step()
 
+  def test_zero1_load(self):
+    device = xm.xla_device()
+
+    model = nn.Linear(32, 32)
+    x = torch.ones((32, 32))
+    x.requires_grad = True
+    model = model.to(device)
+    x = x.to(device)
+    y = model(x).sum()
+    y.backward()
+    xm.mark_step()
+
+    #original optimizer
+    opt = ZeroRedundancyOptimizer(
+        model.parameters(),
+        torch.optim.SGD,
+        lr=0.5,
+        momentum=0.5,
+        grad_clipping=True)
+
+    opt.step()
+
+    #creating a dummy to confirm reload is correct
+    dummy_model = nn.Linear(32, 32)
+    dummy_model = dummy_model.to(device)
+    reloaded_opt = ZeroRedundancyOptimizer(
+        dummy_model.parameters(),
+        torch.optim.SGD,
+        lr=0.1,
+        momentum=0.1,
+        grad_clipping=True)
+
+    orig_opt_state = opt.state_dict()
+
+    #reloading the state dict, not performing torch.save
+    # as it is unnecessary here, the output of torch.load
+    # is same as what is directly used here.
+    reloaded_opt.load_state_dict(orig_opt_state)
+
+    self.assertEqual(reloaded_opt['param_groups'],
+                     orig_opt_state['param_groups'])
+
+    self.assertEqual(reloaded_opt['state'], orig_opt_state['state'])
+
+    self.assertEqual(reloaded_opt['base_state'], orig_opt_state['base_state'])
+
+    self.assertEqual(reloaded_opt['shape_info'], orig_opt_state['shape_info'])
+
 
 def _mp_fn(index):
   device = xm.xla_device()
diff --git a/torch_xla/distributed/zero_redundancy_optimizer.py b/torch_xla/distributed/zero_redundancy_optimizer.py
index b76b53ee42c8..fbbe7d33aa0e 100644
--- a/torch_xla/distributed/zero_redundancy_optimizer.py
+++ b/torch_xla/distributed/zero_redundancy_optimizer.py
@@ -519,6 +519,7 @@ def load_state_dict(self, state_dict):
 
     tmp = self.base_optimizer.state_dict()
     tmp['state'] = base_state
+    tmp['param_groups'] = state_dict['param_groups']
     self.base_optimizer.load_state_dict(tmp)
     if 'sharded_master_weights' in state_dict:
       master_weights = state_dict['sharded_master_weights']