doc

README.md

@@ -170,12 +170,12 @@ ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_con
 
 Our final [model](https://huggingface.co/Dongwei/Qwen-2.5-7B_Base_Math_smalllr), while using different learning rates, loss functions and reward structures, achieves 69.4% accuracy on MATH-500, demonstrating a 17%+ improvement over the base model.
 
-#### Training with a code interpreter
+#### 👨‍💻 Training with a code interpreter
 
 We provide a `code` reward function for executing code generated by the policy during training. Currently, this reward function targets code contests like [Codeforces](https://codeforces.com), where solutions are executed against a set of test cases and the overall success rate is returned as the final reward. To ensure safe execution, we use [E2B](https://e2b.dev) sandboxes, which are fast and cheap to run. To use this reward function, first install the necessary dependencies:
 
 ```shell
-uv pip install -e '.[code]
+uv pip install -e '.[code]'
 ```
 
 Then create a `.env` file and place an API token from E2B within it: