diff --git a/rim/rim.py b/rim/rim.py
index 37717b5..aadcc6f 100644
--- a/rim/rim.py
+++ b/rim/rim.py
@@ -143,16 +143,17 @@ def model_score_fn(x, y, *args, **kwargs):
         self.model_score_fn = model_score_fn
         self.energy_fn = energy_fn
     
-    def initialization(self, observation) -> tuple[list[Tensor], Tensor, Tensor]:
+    def initialization(self, observation, *args, **kwargs) -> tuple[list[Tensor], Tensor, Tensor]:
         """
         From an observation, initialize the parameters to be inferred, x, and the
-        hidden states of the recurrent neural net. 
-        
+        hidden states of the recurrent neural net.
+
         Args:
             observation (Tensor): The observation used for parameter initialization.
-        
+            *args, **kwargs: Additional arguments passed to approximate_inverse_fn
+
         Returns:
-            tuple[list[Tensor], Tensor, Tensor]: A list for the optimization trajectories, 
+            tuple[list[Tensor], Tensor, Tensor]: A list for the optimization trajectories,
                 the initialized parameters x, and the initialized hidden states h.
         """
         batch_size = observation.shape[0]
@@ -161,7 +162,7 @@ def initialization(self, observation) -> tuple[list[Tensor], Tensor, Tensor]:
         if self.initialization_method == "zeros":
             x = torch.zeros((batch_size, self.C, *self.dimensions)).to(self.device)
         elif self.initialization_method == "approximate_inverse":
-            x_param = self.approximate_inverse_fn(observation)
+            x_param = self.approximate_inverse_fn(observation, *args, **kwargs)  # Pass through args
             x = self.inverse_link_function(x_param)
         elif self.initialization_method == "model":
             x = torch.zeros((batch_size, self.C, *self.dimensions)).to(self.device)
@@ -179,15 +180,15 @@ def initialization(self, observation) -> tuple[list[Tensor], Tensor, Tensor]:
     def forward(self, y: Tensor, *args, **kwargs) -> list[Tensor]:
         """
         Perform the forward pass of the RIM optimization.
-        
+
         Args:
             y (Tensor): The observation used for the optimization.
             args and kwargs: Additional arguments and keyword arguments for the score function.
-        
+
         Returns:
             list[Tensor]: The RIM optimization trajectories, represented as a list of parameter x at every iteration of the recurrent series.
         """
-        out, x, h = self.initialization(y)
+        out, x, h = self.initialization(y, *args, **kwargs)  # Pass args to initialization
         for t in range(self.T):
             with torch.no_grad():
                 score = self.model_score_fn(x, y, *args, **kwargs)
@@ -200,13 +201,13 @@ def forward(self, y: Tensor, *args, **kwargs) -> list[Tensor]:
     def predict(self, y: Tensor, *args, **kwargs) -> Tensor:
         """
         Perform a prediction using the RIM optimization.
-        
+
         Args:
             y (Tensor): The observation used for the prediction.
             args and kwargs: Additional arguments and keyword arguments for the score function.
-        
+
         Returns:
-            Tensor: The predicted parameter x after the RIM optimization (in physical space). 
+            Tensor: The predicted parameter x after the RIM optimization (in physical space).
         """
         return self.link_function(self.forward(y, *args, **kwargs)[-1])