diff --git a/deepmd_pt/optimizer/KFWrapper.py b/deepmd_pt/optimizer/KFWrapper.py
index 1ce173ab..d204049c 100644
--- a/deepmd_pt/optimizer/KFWrapper.py
+++ b/deepmd_pt/optimizer/KFWrapper.py
@@ -85,6 +85,41 @@ def update_force(
             self.optimizer.step(error)
         return Etot_predict, force_predict
 
+    def update_denoise_coord(
+        self, inputs: dict, clean_coord: torch.Tensor, update_prefactor: float = 1, mask_loss_coord: bool = True, coord_mask: torch.Tensor = None
+    ) -> None:
+        natoms_sum = inputs['natoms'][0, 0]
+        bs = clean_coord.shape[0]
+        self.optimizer.set_grad_prefactor(natoms_sum * self.atoms_per_group * 3)
+
+        index = self.__sample(self.atoms_selected, self.atoms_per_group, natoms_sum)
+
+        for i in range(index.shape[0]):
+            self.optimizer.zero_grad()
+            model_pred, _, _ = self.model(**inputs, inference_only=True)
+            updated_coord = model_pred['updated_coord']
+            natoms_sum = inputs['natoms'][0, 0]
+            error_tmp = clean_coord[:, index[i]] - updated_coord[:, index[i]]
+            error_tmp = update_prefactor * error_tmp
+            if mask_loss_coord:
+                error_tmp[~coord_mask[:, index[i]]] = 0
+            mask = error_tmp < 0
+            error_tmp[mask] = -1 * error_tmp[mask]
+            error = error_tmp.mean() / natoms_sum
+
+            if self.is_distributed:
+                dist.all_reduce(error)
+                error /= dist.get_world_size()
+
+            tmp_coord_predict = updated_coord[:, index[i]] * update_prefactor
+            tmp_coord_predict[mask] = -update_prefactor * tmp_coord_predict[mask]
+
+            # In order to solve a pytorch bug, reference: https://github.com/pytorch/pytorch/issues/43259
+            (tmp_coord_predict.sum() + updated_coord.sum() * 0).backward()
+            error = error * math.sqrt(bs)
+            self.optimizer.step(error)
+        return model_pred
+
     def __sample(
         self, atoms_selected: int, atoms_per_group: int, natoms: int
     ) -> np.ndarray:
diff --git a/deepmd_pt/optimizer/LKF.py b/deepmd_pt/optimizer/LKF.py
index 42230730..e246ae31 100644
--- a/deepmd_pt/optimizer/LKF.py
+++ b/deepmd_pt/optimizer/LKF.py
@@ -48,7 +48,8 @@ def __init_P(self):
             for param in params:
                 param_num = param.data.nelement()
                 if param_sum + param_num > block_size:
-                    param_nums.append(param_sum)
+                    if param_sum > 0:
+                        param_nums.append(param_sum)
                     param_sum = param_num
                 else:
                     param_sum += param_num
diff --git a/deepmd_pt/train/training.py b/deepmd_pt/train/training.py
index 27e3e7b4..27ae4308 100644
--- a/deepmd_pt/train/training.py
+++ b/deepmd_pt/train/training.py
@@ -199,20 +199,29 @@ def step(_step_id, task_key="Default"):
                 self.optimizer.step()
                 self.scheduler.step()
             elif self.opt_type == "LKF":
-                KFOptWrapper = KFOptimizerWrapper(
-                    self.wrapper, self.optimizer, 24, 6, dist.is_initialized()
-                )
-                _ = KFOptWrapper.update_energy(input_dict, label_dict["energy"])
-                p_energy, p_force = KFOptWrapper.update_force(
-                    input_dict, label_dict["force"]
-                )
-                # [coord, atype, natoms, mapping, shift, selected, box]
-                model_pred = {"energy": p_energy, "force": p_force}
-                module = self.wrapper.module if dist.is_initialized() else self.wrapper
-                loss, more_loss = module.loss[task_key](
+                if isinstance(self.loss, EnergyStdLoss):
+                    KFOptWrapper = KFOptimizerWrapper(
+                        self.wrapper, self.optimizer, 24, 6, dist.is_initialized()
+                    )
+                    _ = KFOptWrapper.update_energy(input_dict, label_dict["energy"])
+                    p_energy, p_force = KFOptWrapper.update_force(
+                        input_dict, label_dict["force"]
+                    )
+                    # [coord, atype, natoms, mapping, shift, selected, box]
+                    model_pred = {"energy": p_energy, "force": p_force}
+                    module = self.wrapper.module if dist.is_initialized() else self.wrapper
+                    loss, more_loss = module.loss[task_key](
+                            model_pred, label_dict, input_dict["natoms"], learning_rate=cur_lr
+                        )
+                elif isinstance(self.loss, DenoiseLoss):
+                    KFOptWrapper = KFOptimizerWrapper(
+                        self.wrapper, self.optimizer, 24, 6, dist.is_initialized()
+                    )
+                    module = self.wrapper.module if dist.is_initialized() else self.wrapper
+                    model_pred = KFOptWrapper.update_denoise_coord(input_dict, label_dict["clean_coord"], 1, module.loss[task_key].mask_loss_coord, label_dict["coord_mask"])
+                    loss, more_loss = module.loss[task_key](
                         model_pred, label_dict, input_dict["natoms"], learning_rate=cur_lr
                     )
-
             else:
                 raise ValueError("Not supported optimizer type '%s'" % self.opt_type)