diff --git a/distributed.rst b/distributed.rst
index c9ed95a8a03..f392729d4b0 100644
--- a/distributed.rst
+++ b/distributed.rst
@@ -12,7 +12,7 @@ There are a few ways you can perform distributed training in
 PyTorch with each method having their advantages in certain use cases:
 
 * `DistributedDataParallel (DDP) <#learn-ddp>`__
-* `Fully Sharded Data Parallel (FSDP) <#learn-fsdp>`__
+* `Fully Sharded Data Parallel (FSDP2) <#learn-fsdp>`__
 * `Tensor Parallel (TP) <#learn-tp>`__
 * `Device Mesh <#device-mesh>`__
 * `Remote Procedure Call (RPC) distributed training <#learn-rpc>`__
@@ -60,28 +60,18 @@ Learn DDP
 
 .. _learn-fsdp:
 
-Learn FSDP
+Learn FSDP2
 ----------
 
 .. grid:: 3
 
      .. grid-item-card:: :octicon:`file-code;1em`
-        Getting Started with FSDP
+        Getting Started with FSDP2
         :link: https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html?utm_source=distr_landing&utm_medium=FSDP_getting_started
         :link-type: url
 
         This tutorial demonstrates how you can perform distributed training
-        with FSDP on a MNIST dataset.
-        +++
-        :octicon:`code;1em` Code
-
-     .. grid-item-card:: :octicon:`file-code;1em`
-        FSDP Advanced
-        :link: https://pytorch.org/tutorials/intermediate/FSDP_advanced_tutorial.html?utm_source=distr_landing&utm_medium=FSDP_advanced
-        :link-type: url
-
-        In this tutorial, you will learn how to fine-tune a HuggingFace (HF) T5
-        model with FSDP for text summarization.
+        with FSDP2 on a transformer model
         +++
         :octicon:`code;1em` Code
 
@@ -196,7 +186,6 @@ Custom Extensions
    intermediate/ddp_tutorial
    intermediate/dist_tuto
    intermediate/FSDP_tutorial
-   intermediate/FSDP_advanced_tutorial
    intermediate/TCPStore_libuv_backend
    intermediate/TP_tutorial
    intermediate/pipelining_tutorial