diff --git a/examples/plot_group_logistic_regression.py b/examples/plot_group_logistic_regression.py
index 5c1e8f71..75854324 100644
--- a/examples/plot_group_logistic_regression.py
+++ b/examples/plot_group_logistic_regression.py
@@ -16,6 +16,8 @@
 from skglm.solvers import GroupProxNewton
 from skglm.utils.data import make_correlated_data, grp_converter
 
+import matplotlib.pyplot as plt
+
 n_features = 30
 X, y, _ = make_correlated_data(
     n_samples=10, n_features=30, random_state=0)
@@ -42,3 +44,23 @@
 # %%
 # Fit check that groups are either all 0 or all non zero
 print(clf.coef_.reshape(-1, grp_size))
+
+# %%
+# Visualise group-level sparsity
+
+coef_by_group = clf.coef_.reshape(-1, grp_size)
+group_norms = np.linalg.norm(coef_by_group, axis=1)
+
+plt.figure(figsize=(8, 4))
+plt.bar(np.arange(n_groups), group_norms)
+plt.xlabel("Group index")
+plt.ylabel("L2 norm of coefficients")
+plt.title("Group Sparsity Pattern")
+plt.tight_layout()
+plt.show()
+
+# %%
+# This plot shows the L2 norm of the coefficients for each group.
+# Groups with a zero norm have been set inactive by the model,
+# illustrating how Group Logistic Regression enforces sparsity at the group level.
+# (Note: This example uses a tiny synthetic dataset, so the pattern has limited interpretability.)