diff --git a/src/torchjd/autogram/_vjp.py b/src/torchjd/autogram/_vjp.py
index 602728fe..08088b1c 100644
--- a/src/torchjd/autogram/_vjp.py
+++ b/src/torchjd/autogram/_vjp.py
@@ -19,7 +19,7 @@ class VJP(ABC):
     """Represents an abstract VJP function."""
 
     @abstractmethod
-    def __call__(self, grad_outputs: PyTree, inputs: PyTree) -> dict[str, Tensor]:
+    def __call__(self, grad_outputs: PyTree, args: PyTree) -> dict[str, Tensor]:
         """
         Computes and returns the dictionary of parameter names to their gradients for the given
         grad_outputs (cotangents) and at the given inputs.
@@ -56,25 +56,25 @@ def __init__(self, module: nn.Module):
         super().__init__(module)
         self.vmapped_vjp = torch.vmap(self._call_on_one_instance)
 
-    def __call__(self, grad_outputs: PyTree, inputs: PyTree) -> dict[str, Tensor]:
-        return self.vmapped_vjp(grad_outputs, inputs)
+    def __call__(self, grad_outputs: PyTree, args: PyTree) -> dict[str, Tensor]:
+        return self.vmapped_vjp(grad_outputs, args)
 
-    def _call_on_one_instance(self, grad_outputs_j: PyTree, inputs_j: PyTree) -> dict[str, Tensor]:
+    def _call_on_one_instance(self, grad_outputs_j: PyTree, args_j: PyTree) -> dict[str, Tensor]:
         # Note: we use unsqueeze(0) to turn a single activation (or grad_output) into a
         # "batch" of 1 activation (or grad_output). This is because some layers (e.g.
         # nn.Flatten) do not work equivalently if they're provided with a batch or with
         # an element of a batch. We thus always provide them with batches, just of a
         # different size.
-        inputs_j = tree_map_only(torch.Tensor, lambda x: x.unsqueeze(0), inputs_j)
+        args_j = tree_map_only(torch.Tensor, lambda x: x.unsqueeze(0), args_j)
         grad_outputs_j = tree_map_only(torch.Tensor, lambda x: x.unsqueeze(0), grad_outputs_j)
 
-        def functional_model_call(primals: dict[str, Parameter]) -> Tensor:
+        def functional_model_call(trainable_params: dict[str, Parameter]) -> Tensor:
             all_state = {
-                **primals,
+                **trainable_params,
                 **dict(self.module.named_buffers()),
                 **self.frozen_params,
             }
-            return torch.func.functional_call(self.module, all_state, inputs_j)
+            return torch.func.functional_call(self.module, all_state, args_j)
 
         vjp_func = torch.func.vjp(functional_model_call, self.trainable_params)[1]