diff --git a/docs/articles/benchmark.md b/docs/articles/benchmark.md
index 14aa203..d01c7ba 100644
--- a/docs/articles/benchmark.md
+++ b/docs/articles/benchmark.md
@@ -61,7 +61,6 @@ Define parameter grids:
 n_values = [1000, 5000]  # Number of documents
 m_values = [500, 1000, 5000, 10000]  # Vocabulary size
 k_values = [10, 50, 100]  # Number of topics
-learning_rate = 0.01
 avg_doc_length = 256 * 256
 ```
 
@@ -72,7 +71,7 @@ benchmark_results = pd.DataFrame()
 
 def benchmark(X, k, device):
     start_time = time.time()
-    model, losses = fit_model(X, k, learning_rate=learning_rate, device=device)
+    model, losses = fit_model(X, k, device=device)
     elapsed_time = time.time() - start_time
 
     return elapsed_time
diff --git a/docs/articles/benchmark.qmd b/docs/articles/benchmark.qmd
index cff9e04..b271c95 100644
--- a/docs/articles/benchmark.qmd
+++ b/docs/articles/benchmark.qmd
@@ -62,7 +62,6 @@ Define parameter grids:
 n_values = [1000, 5000]  # Number of documents
 m_values = [500, 1000, 5000, 10000]  # Vocabulary size
 k_values = [10, 50, 100]  # Number of topics
-learning_rate = 0.01
 avg_doc_length = 256 * 256
 ```
 
@@ -73,7 +72,7 @@ benchmark_results = pd.DataFrame()
 
 def benchmark(X, k, device):
     start_time = time.time()
-    model, losses = fit_model(X, k, learning_rate=learning_rate, device=device)
+    model, losses = fit_model(X, k, device=device)
     elapsed_time = time.time() - start_time
 
     return elapsed_time
diff --git a/docs/articles/get-started.md b/docs/articles/get-started.md
index 48a67ab..57bb1d1 100644
--- a/docs/articles/get-started.md
+++ b/docs/articles/get-started.md
@@ -72,7 +72,7 @@ Fit the topic model and plot the loss curve. There will be a progress
 bar.
 
 ``` python
-model, losses = fit_model(X, k, learning_rate=0.01)
+model, losses = fit_model(X, k)
 
 plot_loss(losses, output_file="loss.png")
 ```
diff --git a/docs/articles/get-started.qmd b/docs/articles/get-started.qmd
index e815dfa..0b4cf40 100644
--- a/docs/articles/get-started.qmd
+++ b/docs/articles/get-started.qmd
@@ -72,7 +72,7 @@ X, true_L, true_F = generate_synthetic_data(n, m, k, avg_doc_length=256 * 256)
 Fit the topic model and plot the loss curve. There will be a progress bar.
 
 ```{python}
-model, losses = fit_model(X, k, learning_rate=0.01)
+model, losses = fit_model(X, k)
 
 plot_loss(losses, output_file="loss.png")
 ```
diff --git a/examples/benchmark.py b/examples/benchmark.py
index 56256db..2cd2b6e 100644
--- a/examples/benchmark.py
+++ b/examples/benchmark.py
@@ -12,7 +12,6 @@
 n_values = [1000, 5000]  # Number of documents
 m_values = [500, 1000, 5000, 10000]  # Vocabulary size
 k_values = [10, 50, 100]  # Number of topics
-learning_rate = 0.01
 avg_doc_length = 256 * 256
 
 
@@ -21,7 +20,7 @@
 
 def benchmark(X, k, device):
     start_time = time.time()
-    model, losses = fit_model(X, k, learning_rate=learning_rate, device=device)
+    model, losses = fit_model(X, k, device=device)
     elapsed_time = time.time() - start_time
 
     return elapsed_time
diff --git a/examples/get-started.py b/examples/get-started.py
index bc1ec16..51b4cf0 100644
--- a/examples/get-started.py
+++ b/examples/get-started.py
@@ -15,7 +15,7 @@
 X, true_L, true_F = generate_synthetic_data(n, m, k, avg_doc_length=256 * 256)
 
 
-model, losses = fit_model(X, k, learning_rate=0.01)
+model, losses = fit_model(X, k)
 
 plot_loss(losses, output_file="loss.png")