diff --git a/examples/cox_regression.py b/examples/cox_regression.py
index 2660b45..c14e68d 100644
--- a/examples/cox_regression.py
+++ b/examples/cox_regression.py
@@ -23,7 +23,7 @@
     tie_approximation="breslow",
 )
 
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 
 plot_path(model, path, X_test, y_test)
 plt.savefig("cox_regression.png")
diff --git a/examples/diabetes.py b/examples/diabetes.py
index cae0580..7705432 100644
--- a/examples/diabetes.py
+++ b/examples/diabetes.py
@@ -27,7 +27,7 @@
     hidden_dims=(10,),
     verbose=True,
 )
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 
 plot_path(model, path, X_test, y_test)
 
diff --git a/examples/friedman.py b/examples/friedman.py
index a07115d..260b99e 100644
--- a/examples/friedman.py
+++ b/examples/friedman.py
@@ -45,7 +45,7 @@ def rrmse(y, y_pred):
             path_multiplier=path_multiplier,
             M=M,
         )
-        path = model.path(X_train, y_train)
+        path = model.path(X_train, y_train, return_state_dicts=True)
         print(
             "rrmse:",
             min(rrmse(y_test, model.load(save).predict(X_test)) for save in path),
diff --git a/examples/friedman/main.py b/examples/friedman/main.py
index 50209d0..833a1ff 100644
--- a/examples/friedman/main.py
+++ b/examples/friedman/main.py
@@ -42,7 +42,9 @@ def rrmse(y, y_pred):
         hidden_dims=(10, 10),
         torch_seed=0,
     )
-    path = model.path(X_train, y_train, X_val=X_val, y_val=y_val)
+    path = model.path(
+        X_train, y_train, X_val=X_val, y_val=y_val, return_state_dicts=True
+    )
     print(
         "rrmse:",
         min(rrmse(y_test, model.load(save).predict(X_test)) for save in path),
diff --git a/examples/generated.py b/examples/generated.py
index 1024b56..e590820 100644
--- a/examples/generated.py
+++ b/examples/generated.py
@@ -57,7 +57,7 @@ def friedman_lockout():
 
     model = LassoNetRegressor(verbose=True, path_multiplier=1.01, hidden_dims=(10, 10))
 
-    path = model.path(X_train, y_train)
+    path = model.path(X_train, y_train, return_state_dicts=True)
     import matplotlib.pyplot as plt
 
     def score(self, X, y, sample_weight=None):
diff --git a/examples/miceprotein.py b/examples/miceprotein.py
index d306b92..af45db0 100755
--- a/examples/miceprotein.py
+++ b/examples/miceprotein.py
@@ -30,69 +30,68 @@
 
 
 model = LassoNetClassifierCV()
-model.path(X_train, y_train)
+model.path(X_train, y_train, return_state_dicts=True)
 print("Best model scored", model.score(X_test, y_test))
 print("Lambda =", model.best_lambda_)
 plot_cv(model, X_test, y_test)
 plt.savefig("miceprotein-cv.png")
-1 / 0
 
 model = LassoNetClassifier()
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein.png")
 
 model = LassoNetClassifier(dropout=0.5)
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_dropout.png")
 
 model = LassoNetClassifier(hidden_dims=(100, 100))
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_deep.png")
 
 model = LassoNetClassifier(hidden_dims=(100, 100), gamma=0.01)
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_deep_l2_weak.png")
 
 model = LassoNetClassifier(hidden_dims=(100, 100), gamma=0.1)
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_deep_l2_strong.png")
 
 model = LassoNetClassifier(hidden_dims=(100, 100), gamma=1)
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_deep_l2_super_strong.png")
 
 model = LassoNetClassifier(hidden_dims=(100, 100), dropout=0.5)
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_deep_dropout.png")
 
 model = LassoNetClassifier(hidden_dims=(100, 100), backtrack=True, dropout=0.5)
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_deep_dropout_backtrack.png")
 
 model = LassoNetClassifier(batch_size=64)
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_64.png")
 
 model = LassoNetClassifier(backtrack=True)
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_backtrack.png")
 
 model = LassoNetClassifier(batch_size=64, backtrack=True)
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_backtrack_64.png")
 
 model = LassoNetClassifier(class_weight=[0.1, 0.2, 0.3, 0.1, 0.3, 0, 0, 0])
-path = model.path(X_train, y_train)
+path = model.path(X_train, y_train, return_state_dicts=True)
 plot_path(model, path, X_test, y_test)
 plt.savefig("miceprotein_weighted.png")