diff --git a/intermediate_source/process_group_cpp_extension_tutorial.rst b/intermediate_source/process_group_cpp_extension_tutorial.rst
index 47379bf881..3c72a9e319 100644
--- a/intermediate_source/process_group_cpp_extension_tutorial.rst
+++ b/intermediate_source/process_group_cpp_extension_tutorial.rst
@@ -25,9 +25,8 @@ Basics
 
 PyTorch collective communications power several widely adopted distributed
 training features, including
-`DistributedDataParallel <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`__,
-`ZeroRedundancyOptimizer <https://pytorch.org/docs/stable/distributed.optim.html#torch.distributed.optim.ZeroRedundancyOptimizer>`__,
-`FullyShardedDataParallel <https://github.com/pytorch/pytorch/blob/master/torch/distributed/_fsdp/fully_sharded_data_parallel.py>`__.
+`DistributedDataParallel <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`__ and
+`ZeroRedundancyOptimizer <https://pytorch.org/docs/stable/distributed.optim.html#torch.distributed.optim.ZeroRedundancyOptimizer>`__.
 In order to make the same collective communication API work with
 different communication backends, the distributed package abstracts collective
 communication operations into a