diff --git a/benchmarl/experiment/experiment.py b/benchmarl/experiment/experiment.py
index e86c52e4..532e8041 100644
--- a/benchmarl/experiment/experiment.py
+++ b/benchmarl/experiment/experiment.py
@@ -734,6 +734,7 @@ def _get_excluded_keys(self, group: str):
         for other_group in self.group_map.keys():
             if other_group != group:
                 excluded_keys += [other_group, ("next", other_group)]
+        excluded_keys += ["info", (group, "info"), ("next", group, "info")]
         return excluded_keys
 
     def _optimizer_loop(self, group: str) -> TensorDictBase:
diff --git a/benchmarl/models/gnn.py b/benchmarl/models/gnn.py
index 5cb0f00e..9c6b6949 100644
--- a/benchmarl/models/gnn.py
+++ b/benchmarl/models/gnn.py
@@ -60,18 +60,23 @@ class Gnn(Model):
         gnn_class (Type[torch_geometric.nn.MessagePassing]): the gnn convolution class to use
         gnn_kwargs (dict, optional): the dict of arguments to pass to the gnn conv class
         position_key (str, optional): if provided, it will need to match a leaf key in the tensordict coming from the env
-            (we suggest to use the "info" dict) representing the agent position. This key will be processed as a
-            node feature (unless exclude_pos_from_node_features=True) and it will be used to construct edge features.
+            (in the `observation_spec`) representing the agent position.
+            To do this, your environment needs to have dictionary observations and one of the keys needs to be `position_key`.
+            This key will be processed as a node feature (unless exclude_pos_from_node_features=True) and it will be used to construct edge features.
             In particular, it will be used to compute relative positions (``pos_node_1 - pos_node_2``) and a
             one-dimensional distance for all neighbours in the graph.
+            If you want to use this feature in a :class:`~benchmarl.models.SequenceModel`, the GNN needs to be first in sequence.
         pos_features (int, optional): Needed when position_key is specified.
             It has to match to the last element of the shape the tensor under position_key.
         exclude_pos_from_node_features (optional, bool): If ``position_key`` is provided,
             wether to use it just to compute edge features or also include it in node features.
         velocity_key (str, optional): if provided, it will need to match a leaf key in the tensordict coming from the env
-            (we suggest to use the "info" dict) representing the agent velocity. This key will be processed as a node feature, and
+            (in the `observation_spec`) representing the agent position.
+            To do this, your environment needs to have dictionary observations and one of the keys needs to be `velocity_key`.
+             This key will be processed as a node feature, and
             it will be used to construct edge features. In particular, it will be used to compute relative velocities
             (``vel_node_1 - vel_node_2``) for all neighbours in the graph.
+            If you want to use this feature in a :class:`~benchmarl.models.SequenceModel`, the GNN needs to be first in sequence.
         vel_features (int, optional): Needed when velocity_key is specified.
             It has to match to the last element of the shape the tensor under velocity_key.
         edge_radius (float, optional): If topology is ``"from_pos"`` the radius to use to build the agent graph.
@@ -170,8 +175,7 @@ def __init__(
         ) and not self.gnn_supports_edge_attrs:
             warnings.warn(
                 "Position key or velocity key provided but GNN class does not support edge attributes. "
-                "These input keys will be ignored. If instead you want to process them as node features, "
-                "just set them (position_key or velocity_key) to null."
+                "These keys will not be used for computing edge features."
             )
         if (
             position_key is not None or velocity_key is not None
@@ -369,10 +373,15 @@ def _forward(self, tensordict: TensorDictBase) -> TensorDictBase:
     def _get_key_terminating_with(self, keys: List[NestedKey], key: str) -> NestedKey:
         for k in keys:
             k_tuple = _unravel_key_to_tuple(k)
-            if k_tuple[-1] == key and self.agent_group in k_tuple:
+            if (
+                k_tuple[-1] == key
+                and self.agent_group in k_tuple
+                and not "next" == k_tuple[0]
+            ):
                 return k
         raise KeyError(
-            f"Key terminating with {key} and containing {self.agent_group} not found in keys: {keys}"
+            f"Key terminating with {key} and containing {self.agent_group} not found in keys: {keys}. "
+            f"If you are using the GNN in a `SequenceModel` and want to use this key, it needs to be the first model."
         )
 
 
diff --git a/docs/source/conf.py b/docs/source/conf.py
index f35b11ff..b9b9a792 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -34,7 +34,7 @@
 intersphinx_mapping = {
     "python": ("https://docs.python.org/3/", None),
     "sphinx": ("https://www.sphinx-doc.org/en/master/", None),
-    "torch": ("https://pytorch.org/docs/master", None),
+    "torch": ("https://pytorch.org/docs/stable/", None),
     "torchrl": ("https://pytorch.org/rl/stable/", None),
     "tensordict": ("https://pytorch.org/tensordict/stable", None),
 }