diff --git a/torchrl/data/replay_buffers/checkpointers.py b/torchrl/data/replay_buffers/checkpointers.py
index 27e91b84e29..6b74834385e 100644
--- a/torchrl/data/replay_buffers/checkpointers.py
+++ b/torchrl/data/replay_buffers/checkpointers.py
@@ -19,6 +19,7 @@
 )
 from tensordict.memmap import MemoryMappedTensor
 from tensordict.utils import _STRDTYPE2DTYPE
+
 from torchrl.data.replay_buffers.utils import (
     _save_pytree,
     Flat2TED,
@@ -93,6 +94,7 @@ def dumps(self, storage, path):
         if is_tensor_collection(_storage):
             if (
                 _storage.is_memmap()
+                and _storage.saved_path
                 and Path(_storage.saved_path).absolute() == Path(path).absolute()
             ):
                 _storage.memmap_refresh_()
@@ -170,9 +172,28 @@ def loads(self, storage, path):
             for hook in self._load_hooks:
                 _storage = hook(_storage, out=dest)
             if not storage.initialized:
-                # this should not be reached if is_pytree=True
-                storage._init(_storage[0])
-                storage._storage.update_(_storage)
+                from torchrl.data.replay_buffers.storages import LazyMemmapStorage
+
+                if (
+                    isinstance(storage, LazyMemmapStorage)
+                    and storage.scratch_dir
+                    and Path(storage.scratch_dir).absolute() == Path(path).absolute()
+                ):
+                    storage._storage = TensorDict.load_memmap(path)
+                    storage.initialized = True
+                else:
+                    # this should not be reached if is_pytree=True
+                    storage._init(_storage[0])
+                    storage._storage.update_(_storage)
+            elif (
+                storage._storage.is_memmap()
+                and storage._storage.saved_path
+                and Path(storage._storage.saved_path).absolute()
+                == Path(path).absolute()
+            ):
+                # If the storage is already where it should be, we don't need to load anything.
+                storage._storage.memmap_refresh_()
+
             else:
                 storage._storage.copy_(_storage)
         storage._len = _len
diff --git a/torchrl/data/replay_buffers/storages.py b/torchrl/data/replay_buffers/storages.py
index 3c540c7ff3e..58b1729296d 100644
--- a/torchrl/data/replay_buffers/storages.py
+++ b/torchrl/data/replay_buffers/storages.py
@@ -917,6 +917,23 @@ class LazyMemmapStorage(LazyTensorStorage):
             has capacity ``3`` if ``ndim=1`` and ``12`` if ``ndim=2``.
             Defaults to ``1``.
 
+    .. note:: When checkpointing a ``LazyMemmapStorage``, one can provide a path identical to where the storage is
+        already stored to avoid executing long copies of data that is already stored on disk.
+        This will only work if the default :class:`~torchrl.data.TensorStorageCheckpointer` checkpointer is used.
+        Example:
+            >>> from tensordict import TensorDict
+            >>> from torchrl.data import TensorStorage, LazyMemmapStorage, ReplayBuffer
+            >>> import tempfile
+            >>> from pathlib import Path
+            >>> import time
+            >>> td = TensorDict(a=0, b=1).expand(1000).clone()
+            >>> # We pass a path that is <main_ckpt_dir>/storage to LazyMemmapStorage
+            >>> rb_memmap = ReplayBuffer(storage=LazyMemmapStorage(10_000_000, scratch_dir="dump/storage"))
+            >>> rb_memmap.extend(td);
+            >>> # Checkpointing in `dump` is a zero-copy, as the data is already in `dump/storage`
+            >>> rb_memmap.dumps(Path("./dump"))
+
+
     Examples:
         >>> data = TensorDict({
         ...     "some data": torch.randn(10, 11),