diff --git a/doc/api/training/sdp_versions/latest.rst b/doc/api/training/sdp_versions/latest.rst
index 31f11a74bd..cdc8ca2128 100644
--- a/doc/api/training/sdp_versions/latest.rst
+++ b/doc/api/training/sdp_versions/latest.rst
@@ -1,5 +1,5 @@
 
-Version 1.1.1 (Latest)
+Version 1.1.0 (Latest)
 ======================
 
 .. toctree::
diff --git a/doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.md b/doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.md
index 0e5feccd4b..bdf5cd8813 100644
--- a/doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.md
+++ b/doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.md
@@ -1,21 +1,3 @@
-# Sagemaker Distributed Data Parallel 1.1.1 Release Notes
-
-* New Features
-* Bug Fixes
-* Known Issues
-
-*New Features:*
-
-* Adds support for PyTorch 1.8.1
-
-*Bug Fixes:*
-
-* Fixes a bug that was causing gradients from one of the worker nodes to be added twice resulting in incorrect `all_reduce` results under some conditions.
-
-*Known Issues:*
-
-* SageMaker distributed data parallel still is not efficient when run using a single node. For the best performance, use multi-node distributed training with `smdistributed.dataparallel`. Use a single node only for experimental runs while preparing your training pipeline.
-
 # Sagemaker Distributed Data Parallel 1.1.0 Release Notes
 
 * New Features
@@ -23,19 +5,19 @@
 * Improvements
 * Known Issues
 
-*New Features:*
+New Features:
 
 * Adds support for PyTorch 1.8.0 with CUDA 11.1 and CUDNN 8
 
-*Bug Fixes:*
+Bug Fixes:
 
 * Fixes crash issue when importing `smdataparallel` before PyTorch
 
-*Improvements:*
+Improvements:
 
 * Update `smdataparallel` name in python packages, descriptions, and log outputs
 
-*Known Issues:*
+Known Issues:
 
 * SageMaker DataParallel is not efficient when run using a single node. For the best performance, use multi-node distributed training with `smdataparallel`. Use a single node only for experimental runs while preparing your training pipeline.