Refresh ObjectIDs in raylet for stopgap GC (#6109)

doc/source/memory-management.rst

@@ -16,7 +16,7 @@ Ray system memory: this is memory used internally by Ray
 
 Application memory: this is memory used by your application
   - **Worker heap**: memory used by your application (e.g., in Python code or TensorFlow), best measured as the *resident set size (RSS)* of your application minus its *shared memory usage (SHR)* in commands such as ``top``.
-  - **Object store memory**: memory used when your application creates objects in the objects store via ``ray.put`` and when returning values from remote functions. Objects are LRU evicted when the store is full. There is an object store server running on each node.
+  - **Object store memory**: memory used when your application creates objects in the objects store via ``ray.put`` and when returning values from remote functions. Objects are LRU evicted when the store is full, prioritizing objects that are no longer in scope on the driver or any worker. There is an object store server running on each node.
   - **Object store shared memory**: memory used when your application reads objects via ``ray.get``. Note that if an object is already present on the node, this does not cause additional allocations. This allows large objects to be efficiently shared among many actors and tasks.
 
 By default, Ray will cap the memory used by Redis at ``min(30% of node memory, 10GiB)``, and object store at ``min(10% of node memory, 20GiB)``, leaving half of the remaining memory on the node available for use by worker heap. You can also manually configure this by setting ``redis_max_memory=<bytes>`` and ``object_store_memory=<bytes>`` on Ray init.