diff --git a/python/gigl/utils/data_splitters.py b/python/gigl/utils/data_splitters.py
index 78c23cba2..3e69516db 100644
--- a/python/gigl/utils/data_splitters.py
+++ b/python/gigl/utils/data_splitters.py
@@ -281,14 +281,18 @@ def __call__(
             )
             node_ids_by_node_type[anchor_node_type].append(anchor_nodes)
         # Second, we go through all node types and split them.
-        # Note the approach here (with `torch.argsort`) isn't the quickest
-        # we could avoid calling `torch.argsort` and do something like:
+        # Note: We could use `torch.argsort` here after normalizing the hash values,
+        # instead of generating masks directly based on comparing the hash values to the split percentages.
+        # e.g.:
         # hash_values = ...
-        # train_mask = hash_values < train_percentage
-        # train = nodes_to_select[train_mask]
-        # That approach is about 2x faster (30s -> 15s on 1B nodes),
-        # but with this `argsort` approach we can be more exact with the number of nodes per split.
-        # The memory usage seems the same across both approaches.
+        # sorted_indices = torch.argsort(hash_values)
+        # test_mask = sorted_indices[:int(hash_values.size(0) * self._num_test)]
+        # test = nodes_to_select[test_mask]
+        # ...
+        # This apporach would let us be exact in the number of nodes per split,
+        # and allow us to allow integer inputs as num_test and num_val.
+        # BUT, it is slower, it takes ~30s instead of ~15s on 1B edges.
+        # The memory usage is the same across both approaches.
 
         # De-dupe this way instead of using `unique` to avoid the overhead of sorting.
         # This approach, goes from ~60s to ~30s on 1B edges.