diff --git a/mindspore/nn/optim/lars.py b/mindspore/nn/optim/lars.py
index e3ab616ddd..2efbb17d0b 100755
--- a/mindspore/nn/optim/lars.py
+++ b/mindspore/nn/optim/lars.py
@@ -80,6 +80,8 @@ class LARS(Optimizer):
                  decay_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name,
                  lars_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name, loss_scale=1.0):
         super(LARS, self).__init__(0.0, [Parameter(Tensor(0.0), name="trivial")])
+        if optimizer.is_group:
+            raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
         self.opt = optimizer
         self.parameters = optimizer.parameters
         self.learning_rate = optimizer.learning_rate
diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py
index d931e5a52f..05560d9739 100755
--- a/mindspore/nn/optim/optimizer.py
+++ b/mindspore/nn/optim/optimizer.py
@@ -81,7 +81,7 @@ class Optimizer(Cell):
             raise ValueError("Optimizer got an empty parameter list.")
 
         if not isinstance(parameters[0], (dict, Parameter)):
-            raise ValueError("Only a list of Parameter or dict can be supported.")
+            raise TypeError("Only a list of Parameter or dict can be supported.")
 
         if isinstance(loss_scale, int):
             loss_scale = float(loss_scale)
@@ -258,9 +258,9 @@ class Optimizer(Cell):
 
             for param in group_param['params']:
                 validator.check_value_type("parameter", param, [Parameter], self.cls_name)
-                if param in params_store:
+                if param.name in params_store:
                     raise RuntimeError(f"The {param.name} parameter has appeared in parameter groups.")
-                params_store.append(param)
+                params_store.append(param.name)
                 self.group_lr.append(Parameter(lr, name="lr_" + param.name))
                 self.group_weight_decay.append(weight_decay_)
 
@@ -298,18 +298,22 @@ class Optimizer(Cell):
             Parameter, single `Parameter` or `list[Parameter]` according to the input type.
         """
         if not isinstance(param, (Parameter, list)):
-            raise TypeError(f"The 'param' only support 'Parameter' or 'list' type.")
+            raise TypeError(f"The parameter only support 'Parameter' or 'list' type.")
 
         if isinstance(param, list):
             lr = []
             for p in param:
                 validator.check_value_type("parameter", p, [Parameter], self.cls_name)
+                if p not in self.parameters:
+                    raise ValueError(f"The parameter {p.name} is not in optimizer.")
                 if self.is_group_lr:
                     index = self.parameters.index(p)
                     lr.append(self.learning_rate[index])
                 else:
                     lr.append(self.learning_rate)
         else:
+            if param not in self.parameters:
+                raise ValueError(f"The parameter {param.name} is not in optimizer.")
             if self.is_group_lr:
                 index = self.parameters.index(param)
                 lr = self.learning_rate[index]
diff --git a/tests/ut/python/nn/optim/test_optimizer.py b/tests/ut/python/nn/optim/test_optimizer.py
index 6594550687..548094840e 100644
--- a/tests/ut/python/nn/optim/test_optimizer.py
+++ b/tests/ut/python/nn/optim/test_optimizer.py
@@ -94,7 +94,7 @@ class TestUnsupportParam():
     """ TestUnsupportParam definition """
 
     def test_optim_init(self):
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             Optimizer(0.1, (1, 2, 3))
 
     def test_AdamWightDecay_init(self):