diff --git a/torchrl/data/map/tree.py b/torchrl/data/map/tree.py
index 2e42cd7864d..684c4f9901b 100644
--- a/torchrl/data/map/tree.py
+++ b/torchrl/data/map/tree.py
@@ -1040,6 +1040,133 @@ def _make_node_map(self, source, dest):
             self.max_size = self.data_map.max_size
 
     def extend(self, rollout, *, return_node: bool = False):
+        """Add a rollout to the forest.
+
+        Nodes are only added to a tree at points where rollouts diverge from
+        each other and at the endpoints of rollouts.
+
+        If there is no existing tree that matches the first steps of the
+        rollout, a new tree is added. Only one node is created, for the final
+        step.
+
+        If there is an existing tree that matches, the rollout is added to that
+        tree. If the rollout diverges from all other rollouts in the tree at
+        some step, a new node is created before the step where the rollouts
+        diverge, and a leaf node is created for the final step of the rollout.
+        If all of the rollout's steps match with a previously added rollout,
+        nothing changes.  If the rollout matches up to a leaf node of a tree but
+        continues beyond it, that node is extended to the end of the rollout,
+        and no new nodes are created.
+
+        Args:
+            rollout (TensorDict): The rollout to add to the forest.
+            return_node (bool, optional): If ``True``, the method returns the
+                added node. Default is ``False``.
+
+        Returns:
+            Tree: The node that was added to the forest. This is only
+                returned if ``return_node`` is True.
+
+        Examples:
+            >>> from torchrl.data import MCTSForest
+            >>> from tensordict import TensorDict
+            >>> import torch
+            >>> forest = MCTSForest()
+            >>> r0 = TensorDict({
+            ...     'action': torch.tensor([1, 2, 3, 4, 5]),
+            ...     'next': {'observation': torch.tensor([123, 392, 989, 809, 847])},
+            ...     'observation': torch.tensor([  0, 123, 392, 989, 809])
+            ... }, [5])
+            >>> r1 = TensorDict({
+            ...     'action': torch.tensor([1, 2, 6, 7]),
+            ...     'next': {'observation': torch.tensor([123, 392, 235,  38])},
+            ...     'observation': torch.tensor([  0, 123, 392, 235])
+            ... }, [4])
+            >>> td_root = r0[0].exclude("next")
+            >>> forest.extend(r0)
+            >>> forest.extend(r1)
+            >>> tree = forest.get_tree(td_root)
+            >>> print(tree)
+            Tree(
+                count=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int32, is_shared=False),
+                index=Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.int64, is_shared=False),
+                node_data=TensorDict(
+                    fields={
+                        observation: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False)},
+                    batch_size=torch.Size([]),
+                    device=cpu,
+                    is_shared=False),
+                node_id=NonTensorData(data=0, batch_size=torch.Size([]), device=None),
+                rollout=TensorDict(
+                    fields={
+                        action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.int64, is_shared=False),
+                        next: TensorDict(
+                            fields={
+                                observation: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.int64, is_shared=False)},
+                            batch_size=torch.Size([2]),
+                            device=cpu,
+                            is_shared=False),
+                        observation: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.int64, is_shared=False)},
+                    batch_size=torch.Size([2]),
+                    device=cpu,
+                    is_shared=False),
+                subtree=Tree(
+                    _parent=NonTensorStack(
+                        [<weakref at 0x716eeb78fbf0; to 'TensorDict' at 0x...,
+                        batch_size=torch.Size([2]),
+                        device=None),
+                    count=Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.int32, is_shared=False),
+                    hash=NonTensorStack(
+                        [4341220243998689835, 6745467818783115365],
+                        batch_size=torch.Size([2]),
+                        device=None),
+                    node_data=LazyStackedTensorDict(
+                        fields={
+                            observation: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.int64, is_shared=False)},
+                        exclusive_fields={
+                        },
+                        batch_size=torch.Size([2]),
+                        device=cpu,
+                        is_shared=False,
+                        stack_dim=0),
+                    node_id=NonTensorStack(
+                        [1, 2],
+                        batch_size=torch.Size([2]),
+                        device=None),
+                    rollout=LazyStackedTensorDict(
+                        fields={
+                            action: Tensor(shape=torch.Size([2, -1]), device=cpu, dtype=torch.int64, is_shared=False),
+                            next: LazyStackedTensorDict(
+                                fields={
+                                    observation: Tensor(shape=torch.Size([2, -1]), device=cpu, dtype=torch.int64, is_shared=False)},
+                                exclusive_fields={
+                                },
+                                batch_size=torch.Size([2, -1]),
+                                device=cpu,
+                                is_shared=False,
+                                stack_dim=0),
+                            observation: Tensor(shape=torch.Size([2, -1]), device=cpu, dtype=torch.int64, is_shared=False)},
+                        exclusive_fields={
+                        },
+                        batch_size=torch.Size([2, -1]),
+                        device=cpu,
+                        is_shared=False,
+                        stack_dim=0),
+                    wins=Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False),
+                    index=None,
+                    subtree=None,
+                    specs=None,
+                    batch_size=torch.Size([2]),
+                    device=None,
+                    is_shared=False),
+                wins=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                hash=None,
+                _parent=None,
+                specs=None,
+                batch_size=torch.Size([]),
+                device=None,
+                is_shared=False)
+        """
         source, dest = (
             rollout.exclude("next").copy(),
             rollout.select("next", *self.action_keys).copy(),