diff --git a/doc/source/ray-references/glossary.rst b/doc/source/ray-references/glossary.rst
index 2564acc16048..3951e04808f8 100644
--- a/doc/source/ray-references/glossary.rst
+++ b/doc/source/ray-references/glossary.rst
@@ -533,16 +533,19 @@ documentation, sorted alphabetically.
         used to combine multiple deployments into “deployment graphs.”
 
     Session
-        The session concept exists on several levels: The experiment execution layer
-        (called Tune Session) and the Data Parallel training layer (called Train
-        Session) if running data-parallel distributed training with Ray Train.
-
-        The session allows access to metadata such as which trial is being run,
-        information about the total number of workers as well as the rank of the
-        current worker. The session is also the interface through which an individual
-        Trainable can interact with the Tune experiment as a whole. This includes uses
-        such as reporting an individual trial’s metrics, saving/loading checkpoints,
-        and retrieving the corresponding dataset shards for each Train worker.
+        - A Ray Train/Tune session: Tune session at the experiment execution layer
+          and Train session at the Data Parallel training layer
+          if running data-parallel distributed training with Ray Train.
+
+          The session allows access to metadata, such as which trial is being run,
+          information about the total number of workers, as well as the rank of the
+          current worker. The session is also the interface through which an individual
+          Trainable can interact with the Tune experiment as a whole. This includes uses
+          such as reporting an individual trial’s metrics, saving/loading checkpoints,
+          and retrieving the corresponding dataset shards for each Train worker.
+
+        - A Ray cluster: in some cases the session also means a :term:`Ray Cluster`.
+          For example, logs of a Ray cluster are stored under ``session_xxx/logs/``.
 
     Spillback
         A task caller schedules a task by first sending a resource request to the