diff --git a/conflictfree/grad_operator.py b/conflictfree/grad_operator.py
index 898bec1..acec228 100644
--- a/conflictfree/grad_operator.py
+++ b/conflictfree/grad_operator.py
@@ -69,7 +69,7 @@ def ConFIG_update(
     grads:Union[torch.Tensor,Sequence[torch.Tensor]],
     weight_model:WeightModel=EqualWeight(),
     length_model:LengthModel=ProjectionLength(),
-    use_latest_square:bool=True,
+    use_least_square:bool=True,
     losses:Optional[Sequence]=None)-> torch.Tensor:
     """
     Performs the standard ConFIG update step.
@@ -81,7 +81,7 @@ def ConFIG_update(
             Defaults to EqualWeight(), which will make the final update gradient not biased towards any gradient.
         length_model (LengthModel, optional): The length model for rescaling the length of the final gradient. 
             Defaults to ProjectionLength(), which will project each gradient vector onto the final gradient vector to get the final length.
-        use_latest_square (bool, optional): Whether to use the latest square method for calculating the best direction. 
+        use_least_square (bool, optional): Whether to use the least square method for calculating the best direction. 
             If set to False, we will directly calculate the pseudo-inverse of the gradient matrix. See `torch.linalg.pinv` and `torch.linalg.lstsq` for more details.
             Recommended to set to True. Defaults to True.
         losses (Optional[Sequence], optional): The losses associated with the gradients. 
@@ -113,7 +113,7 @@ def ConFIG_update(
     with torch.no_grad():
         weights=weight_model.get_weights(gradients=grads,losses=losses,device=grads.device)
         units=torch.nan_to_num((grads/(grads.norm(dim=1)).unsqueeze(1)),0)
-        if use_latest_square:
+        if use_least_square:
             best_direction=torch.linalg.lstsq(units, weights).solution
         else:
             best_direction=torch.linalg.pinv(units)@weights
@@ -185,7 +185,7 @@ class ConFIGOperator(GradientOperator):
             Defaults to ProjectionLength(), which will project each gradient vector onto the final gradient vector to get the final length.
         allow_simplified_model (bool, optional): Whether to allow simplified model for calculating the gradient. 
             If set to True, will use simplified form of ConFIG method when there are only two losses (ConFIG_update_double). Defaults to True.
-        use_latest_square (bool, optional): Whether to use the latest square method for calculating the best direction. 
+        use_least_square (bool, optional): Whether to use the least square method for calculating the best direction. 
             If set to False, we will directly calculate the pseudo-inverse of the gradient matrix. See `torch.linalg.pinv` and `torch.linalg.lstsq` for more details.
             Recommended to set to True. Defaults to True.
     
@@ -213,12 +213,12 @@ def __init__(self,
                  weight_model: WeightModel = EqualWeight(),
                  length_model: LengthModel = ProjectionLength(),
                  allow_simplified_model: bool = True,
-                 use_latest_square: bool = True):
+                 use_least_square: bool = True):
         super().__init__()
         self.weight_model = weight_model
         self.length_model = length_model
         self.allow_simplified_model = allow_simplified_model
-        self.use_latest_square = use_latest_square
+        self.use_least_square = use_least_square
 
     def calculate_gradient(self, grads: Union[torch.Tensor,Sequence[torch.Tensor]], losses: Optional[Sequence] = None)->torch.Tensor:
         """
@@ -245,7 +245,7 @@ def calculate_gradient(self, grads: Union[torch.Tensor,Sequence[torch.Tensor]],
             return ConFIG_update(grads,
                                  weight_model=self.weight_model,
                                  length_model=self.length_model,
-                                 use_latest_square=self.use_latest_square,
+                                 use_least_square=self.use_least_square,
                                  losses=losses)
 
 class PCGradOperator(GradientOperator):
diff --git a/setup.py b/setup.py
index 09ee752..f9dbdf8 100644
--- a/setup.py
+++ b/setup.py
@@ -15,7 +15,7 @@ def get_install_requires():
 
 setuptools.setup(
     name="conflictfree",
-    version="0.1.3",
+    version="0.1.4",
     author="Qiang Liu, Mengyu Chu, Nils Thuerey",
     author_email="qiangliu.7@outlook.com",
     description="Official implementation of Conflict-free Inverse Gradients method",