diff --git a/README.md b/README.md
index 28834bbfaa..3c73302ca7 100644
--- a/README.md
+++ b/README.md
@@ -23,11 +23,11 @@ like how you enjoy real-world milk tea (cheers).
 - ⚡ <span style="color: red; font-weight: bold;">**\[NEW\] AReaLite:**</span> Our new
   release AReaLite is a **light-weight** and **AI-centric** codebase that prioritizes
   better development experiences for AI researchers. As a result, AReaLite delivers most
-  AReaL functionalities and maintains its high performance with much fewer lines of
-  code, supporting users to build their own **agentic** and **RLVR** training workflows
+  AReaL functionalities while maintains its high performance with much fewer lines of
+  code. This allows users to build their own **agentic** and **RLVR** training workflows
   with minimal effort.
 - 🔥 **Asynchronous RL**: With algorithm-system co-design, AReaL supports fully
-  asynchronous RL for **the fastest training**! Experimental support for multi-turn
+  asynchronous RL for **the fastest training speed**! Experimental support for multi-turn
   agentic RL is also provided.
 - 🛠️ **Open & Reproducible**: We continuously release _all code, datasets, and training
   recipes_ for RL training of LLMs.
@@ -69,7 +69,7 @@ New highlights in AReaLite:
 - Instead of the *system-centric* architecture in old AReaL, AReaLite follows an
   **AI-centric** API design that aims to provide the following key features:
 
-  - **Light-weight** & **focused** algorithm and training workflow customization.
+  - **Light-weight** & **easy to write** algorithm and training workflow customization.
   - **Easy to scale up** without knowing system and infrastructure details.
   - **Adaptable and plugable:** Smooth to integrate with other modern AI applications.
 
@@ -106,7 +106,7 @@ Our training scripts will automatically download the dataset (openai/gsm8k) and
 python3 -m arealite.launcher.local examples/arealite/gsm8k_grpo.py --config examples/arealite/configs/gsm8k_grpo.yaml
 ```
 
-On a ray cluster with 2 nodes & 8 GPUs each node, runs (Remember to change paths in YAML
+On a Ray cluster with 2 nodes & 8 GPUs each node, runs (Remember to change paths in the YAML
 file to your own shared storage):
 
 ```