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diff --git a/docs/source/tutorials/quick_start/index.rst b/docs/source/tutorials/quick_start/index.rst
@@ -2,15 +2,15 @@ Quick Start
 ===========
 
 Generative model in GenerativeRL
----------
+--------------------------------
 
 GenerativeRL support easy-to-use APIs for training and deploying generative model.
 We provide a simple example of how to train a diffusion model on the swiss roll dataset in `Colab <https://colab.research.google.com/drive/18yHUAmcMh_7xq2U6TBCtcLKX2y4YvNyk?usp=drive_link>`_.
 
 More usage examples can be found in the folder `grl_pipelines/tutorials/`.
 
 Reinforcement Learning 
-------------
+-----------------------
 
 GenerativeRL provides a simple and flexible interface for training and deploying reinforcement learning agents powered by generative models. Here's an example of how to use the library to train a Q-guided policy optimization (QGPO) agent on the HalfCheetah environment and deploy it for evaluation.
 

diff --git a/docs/source/user_guide/installation.rst b/docs/source/user_guide/installation.rst
@@ -53,5 +53,5 @@ Check whether the installation is successful by running the following command:
 
 .. code-block:: bash
 
-    python -c "import generativerl"
+    python -c "import grl"
 
diff --git a/docs/source/user_guide/training_agents.rst b/docs/source/user_guide/training_agents.rst
@@ -1,5 +1,5 @@
 How to train and deploy reinforcement learning agents
--------------------------------------------------
+-----------------------------------------------------
 
 In GenerativeRL, the RL algorithms are implemented as a class under the ``grl.algorithms`` module, while the agents are implemented as a class under the ``grl.agents`` module.