diff --git a/tensorflow_gnn/keras/layers/convolutions.py b/tensorflow_gnn/keras/layers/convolutions.py
index f19acc89..a4a740a2 100644
--- a/tensorflow_gnn/keras/layers/convolutions.py
+++ b/tensorflow_gnn/keras/layers/convolutions.py
@@ -62,7 +62,7 @@ class SimpleConv(convolution_base.AnyToAnyConvolutionBase):
       combined input features (see combine_type).
     reduce_type: Specifies how to pool the messages to receivers. Defaults to
       `"sum"`, can be any reduce_type understood by `tfgnn.pool()`, including
-      concatenations like `"sum|max"` (but mind the increased dimension of the
+      concatenations like `"sum|mean"` (but mind the increased dimension of the
       result and the growing number of model weights in the next-state layer).
     combine_type: a string understood by tfgnn.combine_values(), to specify how
       the inputs are combined before passing them to the message_fn. Defaults
diff --git a/tensorflow_gnn/models/mt_albis/README.md b/tensorflow_gnn/models/mt_albis/README.md
index 98cbe9eb..917c755b 100644
--- a/tensorflow_gnn/models/mt_albis/README.md
+++ b/tensorflow_gnn/models/mt_albis/README.md
@@ -16,7 +16,7 @@ states from incoming messages. Its main architectural choices are:
   * how to aggregate the incoming messages from each node set:
       * by element-wise averaging (reduce type `"mean"`),
       * by a concatenation of the average with other fixed expressions
-        (e.g., `"mean|max"`, `"mean|sum"`), or
+        (e.g., `"mean|max_no_inf"`, `"mean|sum"`), or
       * with attention, that is, a trained, data-dependent weighting;
   * whether to use residual connections for updating node states;
   * if and how to normalize node states.
diff --git a/tensorflow_gnn/models/mt_albis/layers.py b/tensorflow_gnn/models/mt_albis/layers.py
index 3eb9abe2..4c1df1b8 100644
--- a/tensorflow_gnn/models/mt_albis/layers.py
+++ b/tensorflow_gnn/models/mt_albis/layers.py
@@ -47,7 +47,7 @@ def MtAlbisSimpleConv(  # To be called like a class initializer.  pylint: disabl
       If left unset for init, the tag must be passed at call time.
     reduce_type: Controls how messages are aggregated on an EdgeSet for each
       receiver node; defaults to `"mean"`. Can be any reduce_type understood by
-      `tfgnn.pool()`, including concatenations like `"mean|max"` (but mind the
+      `tfgnn.pool()`, including concatenations like `"mean|sum"` (but mind the
       increased dimension of the result and the growing number of model weights
       in the next-state layer).
     activation: The nonlinearity used on each message before pooling.
@@ -291,10 +291,10 @@ def MtAlbisGraphUpdate(  # To be called like a class initializer.  pylint: disab
     simple_conv_reduce_type: For attention_type `"none"`, controls how messages
       are aggregated on an EdgeSet for each receiver node. Defaults to `"mean"`;
       other recommended values are the concatenations `"mean|sum"`,
-      `"mean|max"`, and `"mean|sum|max"` (but mind the increased output
-      dimension and the corresponding increase in the number of weights in the
-      next-state layer). Technically, can be set to any reduce_type understood
-      by `tfgnn.pool()`.
+      `"mean|max_no_inf"`, and `"mean|sum|max_no_inf"` (but mind the increased
+      output dimension and the corresponding increase in the number of weights
+      in the next-state layer). Technically, can be set to any reduce_type
+      understood by `tfgnn.pool()`.
     simple_conv_use_receiver_state: For attention_type `"none"`, controls
       whether the receiver node state is used in computing each edge's message
       (in addition to the sender node state and possibly an `edge feature`).
diff --git a/tensorflow_gnn/models/vanilla_mpnn/layers.py b/tensorflow_gnn/models/vanilla_mpnn/layers.py
index 5178b2a7..51d525dd 100644
--- a/tensorflow_gnn/models/vanilla_mpnn/layers.py
+++ b/tensorflow_gnn/models/vanilla_mpnn/layers.py
@@ -74,7 +74,7 @@ def VanillaMPNNGraphUpdate(  # To be called like a class initializer.  pylint: d
       this input.
     reduce_type: How to pool the messages from edges to receiver nodes; defaults
       to `"sum"`. Can be any reduce_type understood by `tfgnn.pool()`, including
-      concatenations like `"sum|max"` (but mind the increased dimension of the
+      concatenations like `"sum|mean"` (but mind the increased dimension of the
       result and the growing number of model weights in the next-state layer).
     l2_regularization: The coefficient of L2 regularization for weights and
       biases.