diff --git a/docs/en/user_guides/datasets.md b/docs/en/user_guides/datasets.md
index 1c34b714..fb11b394 100644
--- a/docs/en/user_guides/datasets.md
+++ b/docs/en/user_guides/datasets.md
@@ -101,13 +101,13 @@ afqmc_datasets = [
 
 ```
 
-> Additionally, for binary evaluation metrics (such as accuracy, pass-rate, etc.), you can also set the parameter `k` in conjunction with `n` for [G-Pass@k](http://arxiv.org/abs/2412.13147) evaluation. The formula for G-Pass@k is:
->
-> ```{math}
-> \text{G-Pass@}k_\tau=E_{\text{Data}}\left[ \sum_{j=\lceil \tau \cdot k \rceil}^c \frac{{c \choose j} \cdot {n - c \choose k - j}}{{n \choose k}} \right], 
-> ```
->
-> where $n$ is the number of evaluations, and $c$ is the number of times that passed or were correct out of $n$ runs. An example configuration is as follows:
+Additionally, for binary evaluation metrics (such as accuracy, pass-rate, etc.), you can also set the parameter `k` in conjunction with `n` for [G-Pass@k](http://arxiv.org/abs/2412.13147) evaluation. The formula for G-Pass@k is:
+
+```{math}
+\text{G-Pass@}k_\tau=E_{\text{Data}}\left[ \sum_{j=\lceil \tau \cdot k \rceil}^c \frac{{c \choose j} \cdot {n - c \choose k - j}}{{n \choose k}} \right], 
+```
+
+where $n$ is the number of evaluations, and $c$ is the number of times that passed or were correct out of $n$ runs. An example configuration is as follows:
 
 ```python
 aime2024_datasets = [
diff --git a/docs/zh_cn/user_guides/datasets.md b/docs/zh_cn/user_guides/datasets.md
index 1c8dc616..b1494d3c 100644
--- a/docs/zh_cn/user_guides/datasets.md
+++ b/docs/zh_cn/user_guides/datasets.md
@@ -100,13 +100,13 @@ afqmc_datasets = [
 ]
 ```
 
-> 另外，对于二值评测指标（例如accuracy，pass-rate等），还可以通过设置参数`k`配合`n`进行[G-Pass@k](http://arxiv.org/abs/2412.13147)评测。G-Pass@k计算公式为：
->
-> ```{math}
-> \text{G-Pass@}k_\tau=E_{\text{Data}}\left[ \sum_{j=\lceil \tau \cdot k \rceil}^c \frac{{c \choose j} \cdot {n - c \choose k - j}}{{n \choose k}} \right], 
-> ```
->
-> 其中 $n$ 为评测次数, $c$ 为 $n$ 次运行中通过或正确的次数。配置例子如下：
+另外，对于二值评测指标（例如accuracy，pass-rate等），还可以通过设置参数`k`配合`n`进行[G-Pass@k](http://arxiv.org/abs/2412.13147)评测。G-Pass@k计算公式为：
+
+```{math}
+\text{G-Pass@}k_\tau=E_{\text{Data}}\left[ \sum_{j=\lceil \tau \cdot k \rceil}^c \frac{{c \choose j} \cdot {n - c \choose k - j}}{{n \choose k}} \right], 
+```
+
+其中 $n$ 为评测次数, $c$ 为 $n$ 次运行中通过或正确的次数。配置例子如下：
 
 ```python
 aime2024_datasets = [