diff --git a/README.md b/README.md
index 47b744d..c582f7f 100644
--- a/README.md
+++ b/README.md
@@ -2,10 +2,11 @@
 
 **ReHLine** is designed to be a computationally efficient and practically useful software package for large-scale empirical risk minimization (ERM) problems.
 
-- GitHub repo: [https://github.com/softmin/ReHLine-python](https://github.com/softmin/ReHLine-python) 
 - Documentation: [https://rehline-python.readthedocs.io](https://rehline-python.readthedocs.io)
+- Project homepage: [https://rehline.github.io](https://rehline.github.io)
+- GitHub repo: [https://github.com/softmin/ReHLine-python](https://github.com/softmin/ReHLine-python) 
 - PyPi: [https://pypi.org/project/rehline](https://pypi.org/project/rehline)
-- Open Source: [MIT license](https://opensource.org/licenses/MIT)
+<!-- - Open Source: [MIT license](https://opensource.org/licenses/MIT) -->
 - Paper: [NeurIPS | 2023](https://openreview.net/pdf?id=3pEBW2UPAD)
 
 The **ReHLine** solver has four appealing
@@ -16,6 +17,7 @@ The **ReHLine** solver has four appealing
 - The optimization algorithm has a provable linear convergence rate.
 - The per-iteration computational complexity is linear in the sample size.
 
+<!-- 
 ## 📝 Formulation
 
 **ReHLine** is designed to address the empirical regularized ReLU-ReHU minimization problem, named *ReHLine optimization*, of the following form:
@@ -38,7 +40,7 @@ $$
 
 This formulation has a wide range of applications spanning various fields, including statistics, machine learning, computational biology, and social studies. Some popular examples include SVMs with fairness constraints (FairSVM), elastic net regularized quantile regression (ElasticQR), and ridge regularized Huber minimization (RidgeHuber).
 
-![](./figs/tab.png)
+![](./figs/tab.png) -->
 
 ## ⌛ Benchmark (powered by benchopt)
 
diff --git a/doc/source/README.md b/doc/source/README.md
deleted file mode 100644
index 012c558..0000000
--- a/doc/source/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# **ReHLine**
-
-`ReHLine` is designed to be a computationally efficient and practically useful software package for large-scale ERMs.
-
-The proposed **ReHLine** solver has four appealing
-``linear properties'':
-
-- It applies to any convex piecewise linear-quadratic loss function, including the hinge loss, the check loss, the Huber loss, etc.
-- In addition, it supports linear equality and inequality constraints on the parameter vector.
-- The optimization algorithm has a provable linear convergence rate.
-- The per-iteration computational complexity is linear in the sample size.
-
-## **📮 Formulation**
-`ReHLine` is designed to address the empirical regularized ReLU-ReHU minimization problem, named *ReHLine optimization*, of the following form:
-```math
-  \min_{\mathbf{\beta} \in \mathbb{R}^d} \sum_{i=1}^n \sum_{l=1}^L \text{ReLU}( u_{li} \mathbf{x}_i^\intercal \mathbf{\beta} + v_{li}) + \sum_{i=1}^n \sum_{h=1}^H {\text{ReHU}}_{\tau_{hi}}( s_{hi} \mathbf{x}_i^\intercal \mathbf{\beta} + t_{hi}) + \frac{1}{2} \| \mathbf{\beta} \|_2^2, \qquad \text{ s.t. } \mathbf{A} \mathbf{\beta} + \mathbf{b} \geq \mathbf{0},
-```
-where $\mathbf{U} = (u_{li}),\mathbf{V} = (v_{li}) \in \mathbb{R}^{L \times n}$ and $\mathbf{S} = (s_{hi}),\mathbf{T} = (t_{hi}),\mathbf{\tau} = (\tau_{hi}) \in \mathbb{R}^{H \times n}$ are the ReLU-ReHU loss parameters, and $(\mathbf{A},\mathbf{b})$ are the constraint parameters. This formulation has a wide range of applications spanning various fields, including statistics, machine learning, computational biology, and social studies. Some popular examples include SVMs with fairness constraints (FairSVM), elastic net regularized quantile regression (ElasticQR), and ridge regularized Huber minimization (RidgeHuber).
-
-![](./figs/tab.png)
-
-## 📚 **Benchmark (powered by benchopt)**
-Some existing problems of recent interest in statistics and machine learning can be solved by `ReHLine`. 
-| Problem   |      Results      |
-|---------- |:-----------------:|
-|[FairSVM](./python/benchmark/benchmark_FairSVM/README.md) | [Result](./python/benchmark/benchmark_FairSVM/outputs/benchmark_FairSVM.html)|
-|[ElasticQR](./python/benchmark/benchmark_QR/README.md) | [Result](./python/benchmark/benchmark_QR/outputs/benchmark_QR.html)|
-|[RidgeHuber](./python/benchmark/benchmark_Huber/README.md) | [Result](./python/benchmark/benchmark_Huber/outputs/benchmark_Huber.html)|
-|[SVM](./python/benchmark/benchmark_SVM/README.md) | [Result](./python/benchmark/benchmark_SVM/outputs/benchmark_SVM.html)|
-
-## 🧾 **Overview of Results**
-
-![](./figs/res.png)
diff --git a/pyproject.toml b/pyproject.toml
index 0d66bed..7318eb1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "rehline"
-version = "0.0.4.dev0"
+version = "0.0.4"
 description = "Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence"
 authors = [
   {name = "Ben Dai", email = "bendai@cuhk.edu.hk"},
diff --git a/setup.py b/setup.py
index 21947dc..aafb3a2 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
 from setuptools import setup
 from setuptools.command.build_ext import build_ext
 
-__version__ = "0.0.4.dev0"
+__version__ = "0.0.4"
 
 # The main interface is through Pybind11Extension.
 # * You can add cxx_std=11/14/17, and then build_ext can be removed.
@@ -66,7 +66,7 @@ def __str__(self) -> str:
     version=__version__,
     author=["Ben Dai", "Yixuan Qiu"],
     author_email="bendai@cuhk.edu.hk",
-    url="https://github.com/softmin/ReHLine-python",
+    url="https://rehline-python.readthedocs.io/en/latest/",
     description="Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence",
     packages=["rehline"],
     # install_requires=["requests", "pybind11", "numpy", "scipy", "scikit-learn"],