diff --git a/torchrl/data/replay_buffers/replay_buffers.py b/torchrl/data/replay_buffers/replay_buffers.py
index ebeea7dcf6b..8c928ced9b6 100644
--- a/torchrl/data/replay_buffers/replay_buffers.py
+++ b/torchrl/data/replay_buffers/replay_buffers.py
@@ -883,40 +883,21 @@ def add(self, data: TensorDictBase) -> int:
         return index
 
     def extend(self, tensordicts: TensorDictBase) -> torch.Tensor:
+        _data = tensordicts
+
+        if self._transform is not None:
+            _data = self._transform.inv(_data)
 
         tensordicts = TensorDict(
-            {"_data": tensordicts},
-            batch_size=torch.Size([]),
+            {"_data": _data},
+            batch_size=_data.shape[:1],
+            device=_data.device,
+            names=_data.names[:1] if _data._has_names() else None,
         )
-        if tensordicts.batch_dims > 1:
-            # we want the tensordict to have one dimension only. The batch size
-            # of the sampled tensordicts can be changed thereafter
-            if not isinstance(tensordicts, LazyStackedTensorDict):
-                tensordicts = tensordicts.clone(recurse=False)
-            else:
-                tensordicts = tensordicts.contiguous()
-            # we keep track of the batch size to reinstantiate it when sampling
-            if "_rb_batch_size" in tensordicts.keys():
-                raise KeyError(
-                    "conflicting key '_rb_batch_size'. Consider removing from data."
-                )
-            shape = torch.tensor(tensordicts.batch_size[1:]).expand(
-                tensordicts.batch_size[0], tensordicts.batch_dims - 1
-            )
-            tensordicts.set("_rb_batch_size", shape)
 
-        if self._transform is not None:
-            data = self._transform.inv(tensordicts.get("_data"))
-            tensordicts._set_str("_data", data, validated=True, inplace=False)
-            if data.device is not None:
-                tensordicts = tensordicts.to(data.device)
-
-        _data = tensordicts.get("_data")
-        tensordicts.batch_size = _data.batch_size[:1]
-        tensordicts.names = _data.names[:1]
         tensordicts.set(
             "index",
-            torch.zeros(tensordicts.shape, device=tensordicts.device, dtype=torch.int),
+            torch.zeros(tensordicts.shape, device=tensordicts.device, dtype=torch.long),
         )
 
         index = super()._extend(tensordicts)
@@ -989,10 +970,12 @@ def _sample(self, batch_size: int) -> Tuple[Any, dict]:
         with self._replay_lock:
             index, info = self._sampler.sample(self._storage, batch_size)
             info["index"] = index
-            data = self._storage[index]
+            data = self._storage.get(index)
         if not isinstance(index, INT_CLASSES):
             data = self._collate_fn(data)
-
+        if self._transform is not None and len(self._transform):
+            with data.unlock_(), _set_dispatch_td_nn_modules(True):
+                data = self._transform(data)
         return data, info
 
 
diff --git a/torchrl/data/replay_buffers/storages.py b/torchrl/data/replay_buffers/storages.py
index 1a0119cd50e..f975bf34b86 100644
--- a/torchrl/data/replay_buffers/storages.py
+++ b/torchrl/data/replay_buffers/storages.py
@@ -734,7 +734,8 @@ def _init(
                 .to(self.device)
             )
             if self.names is not None:
-                out.names = self.names
+                names = self.names + [None] * (out.batch_dims - len(self.names))
+                out.refine_names(*names)
         else:
             # if Tensor, we just create a MemoryMappedTensor of the desired shape, device and dtype
             out = tree_map(