diff --git a/cca_zoo/models/_stochastic/_base.py b/cca_zoo/models/_stochastic/_base.py
index 76422727..33c0d546 100644
--- a/cca_zoo/models/_stochastic/_base.py
+++ b/cca_zoo/models/_stochastic/_base.py
@@ -86,7 +86,7 @@ def fit(self, views: Iterable[np.ndarray], y=None, **kwargs):
             )
         self.track = []
         self.weights = [
-            np.random.rand(view.shape[1], self.latent_dims) for view in views
+            self.random_state.normal(0, 1, size=(view.shape[1],self.latent_dims)) for view in views
         ]
         # normalize weights
         self.weights = [weight / np.linalg.norm(weight, axis=0) for weight in self.weights]
diff --git a/cca_zoo/test/test_models.py b/cca_zoo/test/test_models.py
index 98b08f4f..4ae60a97 100644
--- a/cca_zoo/test/test_models.py
+++ b/cca_zoo/test/test_models.py
@@ -226,14 +226,14 @@ def test_stochastic_pls():
     from torch import manual_seed
     manual_seed(42)
     pls = PLS(latent_dims=3).fit((X, Y))
-    ipls = IncrementalPLS(latent_dims=3, epochs=150, simple=False, batch_size=10).fit(
+    ipls = IncrementalPLS(latent_dims=3, epochs=150, simple=False, batch_size=10, random_state=1).fit(
         (X, Y)
     )
-    spls = PLSStochasticPower(latent_dims=3, epochs=150, batch_size=10, learning_rate=1e-2).fit(
+    spls = PLSStochasticPower(latent_dims=3, epochs=150, batch_size=10, learning_rate=1e-2, random_state=1).fit(
         (X, Y)
     )
-    egpls = PLSEigenGame(latent_dims=3, epochs=150, batch_size=10, learning_rate=1e-2).fit((X, Y))
-    ghapls = PLSGHAGEP(latent_dims=3, epochs=150, batch_size=10, learning_rate=1e-2).fit((X, Y))
+    egpls = PLSEigenGame(latent_dims=3, epochs=150, batch_size=10, learning_rate=1e-2, random_state=1).fit((X, Y))
+    ghapls = PLSGHAGEP(latent_dims=3, epochs=150, batch_size=10, learning_rate=1e-2, random_state=1).fit((X, Y))
     pls_score = pls.score((X, Y))
     ipls_score = ipls.score((X, Y))
     spls_score = spls.score((X, Y))