diff --git a/flaml/tune/README.md b/flaml/tune/README.md
index 6b7e0b93a..c2b3fced3 100644
--- a/flaml/tune/README.md
+++ b/flaml/tune/README.md
@@ -1,9 +1,9 @@
 # Economical Hyperparameter Optimization
 
 `flaml.tune` is a module for economical hyperparameter tuning. It frees users from manually tuning many hyperparameters for a software, such as machine learning training procedures. 
-The API is compatible with ray tune.
+It can be used standalone, or together with ray tune or nni.
 
-Example:
+* Example for sequential tuning (recommended when compute resource is limited and each trial can consume all the resources):
 
 ```python
 # require: pip install flaml[blendsearch]
@@ -41,7 +41,8 @@ print(analysis.best_trial.last_result)  # the best trial's result
 print(analysis.best_config) # the best config
 ```
 
-Or, using ray tune's API:
+* Example for using ray tune's API:
+
 ```python
 # require: pip install flaml[blendsearch] ray[tune]
 from ray import tune as raytune
@@ -71,14 +72,19 @@ analysis = raytune.run(
     time_budget_s=60,   # the time budget in seconds
     local_dir='logs/',  # the local directory to store logs
     search_alg=CFO(points_to_evaluate=[{'x':1}]) # or BlendSearch
-    # other algo example: raytune.create_searcher('optuna'),
     )
 
 print(analysis.best_trial.last_result)  # the best trial's result
 print(analysis.best_config) # the best config
 ```
 
-For more examples, please check out 
+* Example for using NNI: An example of using BlendSearch with NNI can be seen in [test](https://github.com/microsoft/FLAML/tree/main/test/nni). CFO can be used as well in a similar manner. To run the example, first make sure you have [NNI](https://nni.readthedocs.io/en/stable/) installed, then run:
+
+```shell
+$nnictl create --config ./config.yml
+```
+
+* For more examples, please check out 
 [notebooks](https://github.com/microsoft/FLAML/tree/main/notebook/).
 
 
@@ -159,12 +165,6 @@ Recommended scenario: cost-related hyperparameters exist, a low-cost
 initial point is known, and the search space is complex such that local search
 is prone to be stuck at local optima.
 
-An example of using BlendSearch with NNI can be seen in [test](https://github.com/microsoft/FLAML/tree/main/test/nni), CFO can be used with NNI as well in a similar manner. To run the example, first make sure you have [NNI](https://nni.readthedocs.io/en/stable/) installed, then run:
-
-```shell
-$nnictl create --config ./config.yml
-```
-
 For more technical details, please check our papers.
 
 * [Frugal Optimization for Cost-related Hyperparameters](https://arxiv.org/abs/2005.01571). Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.