diff --git a/functorch/_src/make_functional.py b/functorch/_src/make_functional.py
index 42268ea981..5c58d595d8 100644
--- a/functorch/_src/make_functional.py
+++ b/functorch/_src/make_functional.py
@@ -257,12 +257,12 @@ def __init__(self, stateless_model, param_names, buffer_names,
         self.all_names_map.update(buffer_names_map)
 
     @staticmethod
-    def _create_from(model, disable_params_grad=False):
+    def _create_from(model, disable_autograd_tracking=False):
         # TODO: We don't need to copy the model to create a stateless copy
         model_copy = copy.deepcopy(model)
         params, param_names, param_names_map = extract_weights(model_copy)
         buffers, buffer_names, buffer_names_map = extract_buffers(model_copy)
-        if disable_params_grad:
+        if disable_autograd_tracking:
             for param in params:
                 param.requires_grad_(False)
         return (
@@ -297,11 +297,11 @@ def __init__(self, stateless_model, param_names, names_map):
         self.names_map = names_map
 
     @staticmethod
-    def _create_from(model, disable_params_grad=False):
+    def _create_from(model, disable_autograd_tracking=False):
         # TODO: We don't need to copy the model to create a stateless copy
         model_copy = copy.deepcopy(model)
         params, param_names, names_map = extract_weights(model_copy)
-        if disable_params_grad:
+        if disable_autograd_tracking:
             for param in params:
                 param.requires_grad_(False)
         return FunctionalModule(model_copy, param_names, names_map), params
@@ -316,7 +316,7 @@ def forward(self, params, *args, **kwargs):
             _swap_state(self.stateless_model, self.names_map, old_state)
 
 
-def make_functional(model: nn.Module, disable_params_grad: bool = False):
+def make_functional(model: nn.Module, disable_autograd_tracking: bool = False):
     """make_functional(model) -> func, params
 
     Given a ``torch.nn.Module``, :func:`make_functional` extracts the state
@@ -361,20 +361,26 @@ def compute_loss(params, x, t):
 
     Args:
         model (torch.nn.Module): Input model.
-        disable_params_grad (bool): Flag to disable gradients for output parameters.
-            It could be helpful to set this flag to True if gradient computations only use the functorch API.
-            Disabling gradients also helps to avoid accumulating them in the backprop graph and to prevent
-            keeping them in memory. Default: False.
+        disable_autograd_tracking (bool): Flag to disable gradients tracking for output parameters.
+            The returned params are unrelated to the set of params from the original model. If False (default),
+            the params will have ``requires_grad=True`` on them (aka they will be trackable with regular
+            PyTorch autograd), matching the requires_grad-ness of the params from the original model.
+            Otherwise, the returned params will have ``requires_grad=False``. Default, False.
+            If you plan on using regular PyTorch autograd (e.g., if you want to call ``.backward()`` or
+            ``torch.autograd.grad()``, then set ``disable_autograd_tracking=False``.
+            Otherwise, if you're only planning on using functorch's gradient transforms,
+            then please set ``disable_autograd_tracking=True`` to avoid unnecessarily tracking
+            history with PyTorch autograd.
 
     """
     buffers = list(model.buffers())
     if len(buffers) > 0:
         raise RuntimeError('make_functional(model): `model` has buffers. Please use '
                            'make_functional_with_buffers(model) instead.')
-    return FunctionalModule._create_from(model, disable_params_grad=disable_params_grad)
+    return FunctionalModule._create_from(model, disable_autograd_tracking=disable_autograd_tracking)
 
 
-def make_functional_with_buffers(model: nn.Module, disable_params_grad: bool = False):
+def make_functional_with_buffers(model: nn.Module, disable_autograd_tracking: bool = False):
     """make_functional_with_buffers(model) -> func, params, buffers
 
     Given a ``torch.nn.Module``, make_functional_with_buffers extracts the
@@ -416,13 +422,19 @@ def compute_loss(params, buffers, x, t):
 
     Args:
         model (torch.nn.Module): Input model.
-        disable_params_grad (bool): Flag to disable gradients for output parameters.
-            It could be helpful to set this flag to True if gradient computations only use the functorch API.
-            Disabling gradients also helps to avoid accumulating them in the backprop graph and to prevent
-            keeping them in memory. Default: False.
+        disable_autograd_tracking (bool): Flag to disable gradients tracking for output parameters.
+            The returned params are unrelated to the set of params from the original model. If False (default),
+            the params will have ``requires_grad=True`` on them (aka they will be trackable with regular
+            PyTorch autograd), matching the requires_grad-ness of the params from the original model.
+            Otherwise, the returned params will have ``requires_grad=False``. Default, False.
+            If you plan on using regular PyTorch autograd (e.g., if you want to call ``.backward()`` or
+            ``torch.autograd.grad()``, then set ``disable_autograd_tracking=False``.
+            Otherwise, if you're only planning on using functorch's gradient transforms,
+            then please set ``disable_autograd_tracking=True`` to avoid unnecessarily tracking
+            history with PyTorch autograd.
 
     """
-    return FunctionalModuleWithBuffers._create_from(model, disable_params_grad=disable_params_grad)
+    return FunctionalModuleWithBuffers._create_from(model, disable_autograd_tracking=disable_autograd_tracking)
 
 
 def transpose_stack(tuple_of_tuple_of_tensors):