diff --git a/torchrl/collectors/utils.py b/torchrl/collectors/utils.py
index 91b66b94420..d777da3de2a 100644
--- a/torchrl/collectors/utils.py
+++ b/torchrl/collectors/utils.py
@@ -210,8 +210,20 @@ def nest(x, splits=splits):
 
             layout = as_nested if as_nested is not bool else None
 
-            def nest(*x):
-                return torch.nested.nested_tensor(list(x), layout=layout)
+            if torch.__version__ < "2.4":
+                # Layout must be True, there is no other layout available
+                if layout not in (True,):
+                    raise RuntimeError(
+                        f"layout={layout} is only available for torch>=v2.4"
+                    )
+
+                def nest(*x):
+                    return torch.nested.nested_tensor(list(x))
+
+            else:
+
+                def nest(*x):
+                    return torch.nested.nested_tensor(list(x), layout=layout)
 
             return out_splits[0]._fast_apply(
                 nest,
diff --git a/torchrl/data/tensor_specs.py b/torchrl/data/tensor_specs.py
index 6f75f207293..04c24cb8d57 100644
--- a/torchrl/data/tensor_specs.py
+++ b/torchrl/data/tensor_specs.py
@@ -3982,7 +3982,7 @@ def encode(
         if isinstance(vals, TensorDict):
             out = vals.empty()  # create and empty tensordict similar to vals
         else:
-            out = TensorDict({}, torch.Size([]), _run_checks=False)
+            out = TensorDict._new_unsafe({}, torch.Size([]))
         for key, item in vals.items():
             if item is None:
                 raise RuntimeError(
@@ -4047,13 +4047,12 @@ def rand(self, shape=None) -> TensorDictBase:
         for key, item in self.items():
             if item is not None:
                 _dict[key] = item.rand(shape)
-        return TensorDict(
+        # No need to run checks since we know Composite is compliant with
+        # TensorDict requirements
+        return TensorDict._new_unsafe(
             _dict,
             batch_size=torch.Size([*shape, *self.shape]),
             device=self._device,
-            # No need to run checks since we know Composite is compliant with
-            # TensorDict requirements
-            _run_checks=False,
         )
 
     def keys(
diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py
index d9f80ffa391..47f93f09779 100644
--- a/torchrl/envs/gym_like.py
+++ b/torchrl/envs/gym_like.py
@@ -343,8 +343,9 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase:
                     for key, val in TensorDict(obs_dict, []).items(True, True)
                 )
         else:
-            tensordict_out = TensorDict(
-                obs_dict, batch_size=tensordict.batch_size, _run_checks=False
+            tensordict_out = TensorDict._new_unsafe(
+                obs_dict,
+                batch_size=tensordict.batch_size,
             )
         if self.device is not None:
             tensordict_out = tensordict_out.to(self.device, non_blocking=True)
@@ -377,10 +378,9 @@ def _reset(
 
         source = self.read_obs(obs)
 
-        tensordict_out = TensorDict(
+        tensordict_out = TensorDict._new_unsafe(
             source=source,
             batch_size=self.batch_size,
-            _run_checks=not self.validated,
         )
         if self.info_dict_reader and info is not None:
             for info_dict_reader in self.info_dict_reader:
diff --git a/torchrl/envs/libs/brax.py b/torchrl/envs/libs/brax.py
index 3b4b4cee224..c86ba9a543c 100644
--- a/torchrl/envs/libs/brax.py
+++ b/torchrl/envs/libs/brax.py
@@ -321,7 +321,7 @@ def _reset(self, tensordict: TensorDictBase = None, **kwargs) -> TensorDictBase:
         state["reward"] = state.get("reward").view(*self.reward_spec.shape)
         state["done"] = state.get("done").view(*self.reward_spec.shape)
         done = state["done"].bool()
-        tensordict_out = TensorDict(
+        tensordict_out = TensorDict._new_unsafe(
             source={
                 "observation": state.get("obs"),
                 # "reward": reward,
@@ -331,7 +331,6 @@ def _reset(self, tensordict: TensorDictBase = None, **kwargs) -> TensorDictBase:
             },
             batch_size=self.batch_size,
             device=self.device,
-            _run_checks=False,
         )
         return tensordict_out
 
@@ -357,7 +356,7 @@ def _step_without_grad(self, tensordict: TensorDictBase):
         next_state.set("done", next_state.get("done").view(self.reward_spec.shape))
         done = next_state["done"].bool()
         reward = next_state["reward"]
-        tensordict_out = TensorDict(
+        tensordict_out = TensorDict._new_unsafe(
             source={
                 "observation": next_state.get("obs"),
                 "reward": reward,
@@ -367,7 +366,6 @@ def _step_without_grad(self, tensordict: TensorDictBase):
             },
             batch_size=self.batch_size,
             device=self.device,
-            _run_checks=False,
         )
         return tensordict_out
 
@@ -396,7 +394,7 @@ def _step_with_grad(self, tensordict: TensorDictBase):
         next_state.get("pipeline_state").update(dict(zip(qp_keys, next_qp_values)))
 
         # build result
-        tensordict_out = TensorDict(
+        tensordict_out = TensorDict._new_unsafe(
             source={
                 "observation": next_obs,
                 "reward": next_reward,
@@ -406,7 +404,6 @@ def _step_with_grad(self, tensordict: TensorDictBase):
             },
             batch_size=self.batch_size,
             device=self.device,
-            _run_checks=False,
         )
         return tensordict_out
 
diff --git a/torchrl/objectives/sac.py b/torchrl/objectives/sac.py
index 9594895394a..51017384dbe 100644
--- a/torchrl/objectives/sac.py
+++ b/torchrl/objectives/sac.py
@@ -632,7 +632,7 @@ def _actor_loss(
     @property
     @_cache_values
     def _cached_target_params_actor_value(self):
-        return TensorDict(
+        return TensorDict._new_unsafe(
             {
                 "module": {
                     "0": self.target_actor_network_params,
@@ -640,7 +640,6 @@ def _cached_target_params_actor_value(self):
                 }
             },
             torch.Size([]),
-            _run_checks=False,
         )
 
     def _qvalue_v1_loss(