diff --git a/README.md b/README.md
index e9facef64d..e08b1d07a8 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@ Its ambitions are:
 
 ## Features
 > _The codebase is currently under active development._
-> _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) and [What's New in 0.6](https://docs.monai.io/en/latest/whatsnew_0_6.html) of the current milestone release._
+> _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) and [What's New](https://docs.monai.io/en/latest/whatsnew.html) of the current milestone release._
 
 - flexible pre-processing for multi-dimensional medical imaging data;
 - compositional & portable APIs for ease of integration in existing workflows;
diff --git a/docs/source/whatsnew_0_7.md b/docs/source/whatsnew_0_7.md
index 8d0f3947f7..5a0a82130d 100644
--- a/docs/source/whatsnew_0_7.md
+++ b/docs/source/whatsnew_0_7.md
@@ -21,8 +21,8 @@ a performance enhancement study.
 
 With the performance profiling and enhancements, several typical use cases were studied to
 improve the training efficiency.  The following figure shows that fast
-training using MONAI can be 20 times faster than a regular baseline ([learn
-more](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_training_tutorial.ipynb)).
+training using MONAI can be `200` times faster than a regular baseline ([learn
+more](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_training_tutorial.ipynb)), and it's `20` times faster than the MONAI v0.6 fast training solution.
 ![fast_training](../images/fast_training.png)
 
 ## Major usability improvements in `monai.transforms` for NumPy/PyTorch inputs and backends