diff --git a/tensordict/nn/params.py b/tensordict/nn/params.py
index f0a716fe8..6cedbf41d 100644
--- a/tensordict/nn/params.py
+++ b/tensordict/nn/params.py
@@ -234,6 +234,11 @@ class TensorDictParams(TensorDictBase, nn.Module):
             If ``no_convert`` is ``True`` and if non-parameters are present, they
             will be registered as buffers.
             Defaults to ``False``.
+        lock (bool): if ``True``, the tensordict hosted by TensorDictParams will
+            be locked. This can be useful to avoid unwanted modifications, but
+            also restricts the operations that can be done over the object (and
+            can have significant performance impact when `unlock_()` is required).
+            Defaults to ``False``.
 
     Examples:
         >>> from torch import nn
@@ -273,7 +278,9 @@ class TensorDictParams(TensorDictBase, nn.Module):
 
     """
 
-    def __init__(self, parameters: TensorDictBase, *, no_convert=False):
+    def __init__(
+        self, parameters: TensorDictBase, *, no_convert=False, lock: bool = False
+    ):
         super().__init__()
         if isinstance(parameters, TensorDictParams):
             parameters = parameters._param_td
@@ -283,7 +290,10 @@ def __init__(self, parameters: TensorDictBase, *, no_convert=False):
             func = _maybe_make_param
         else:
             func = _maybe_make_param_or_buffer
-        self._param_td = _apply_leaves(self._param_td, lambda x: func(x)).lock_()
+        self._param_td = _apply_leaves(self._param_td, lambda x: func(x))
+        self._lock = lock
+        if lock:
+            self._param_td.lock_()
         self._reset_params()
         self._is_locked = False
         self._locked_tensordicts = []
@@ -307,18 +317,21 @@ def _apply_get_post_hook(self, val):
     def _reset_params(self):
         parameters = self._param_td
         param_keys = []
+        params = []
         buffer_keys = []
+        buffers = []
         for key, value in parameters.items(True, True):
+            # flatten key
+            if isinstance(key, tuple):
+                key = "_".join(key)
             if isinstance(value, nn.Parameter):
                 param_keys.append(key)
+                params.append(value)
             else:
                 buffer_keys.append(key)
-        self.__dict__["_parameters"] = (
-            parameters.select(*param_keys).flatten_keys("_").to_dict()
-        )
-        self.__dict__["_buffers"] = (
-            parameters.select(*buffer_keys).flatten_keys("_").to_dict()
-        )
+                buffers.append(value)
+        self.__dict__["_parameters"] = dict(zip(param_keys, params))
+        self.__dict__["_buffers"] = dict(zip(buffer_keys, buffers))
 
     @classmethod
     def __torch_function__(