diff --git a/liana/tests/test_causalnet.py b/liana/tests/test_causalnet.py
index f8ca530..d43cb5a 100644
--- a/liana/tests/test_causalnet.py
+++ b/liana/tests/test_causalnet.py
@@ -28,8 +28,8 @@ def test_caulsalnet():
                                      verbose=False,
                                      solver='scipy',
                                      seed=1337,
-                                     max_runs=20,
-                                     stable_runs=10,
+                                     max_runs=50,
+                                     stable_runs=20,
                                      )
 
     assert problem.weights == [1.0, 0.01, 1.0, 1.0]
@@ -46,7 +46,9 @@ def test_causalnet_noweights():
                                      output_scores,
                                      node_weights={"N1": 1, "N2": 0},
                                      verbose=False,
-                                     solver='scipy'
+                                     solver='scipy',
+                                     max_runs=50,
+                                     stable_runs=20,
                                      )
     assert df_res['source_pred_val'].values.sum() == 8
     assert df_res['target_pred_val'].values.sum() == 9
diff --git a/liana/tests/test_estimate_metalinks.py b/liana/tests/test_estimate_metalinks.py
index 942290f..2432f9f 100644
--- a/liana/tests/test_estimate_metalinks.py
+++ b/liana/tests/test_estimate_metalinks.py
@@ -22,5 +22,5 @@ def test_estimate_metalinks():
                                min_n=2)
     assert np.isin(['metabolite', 'receptor'], list(mdata.mod.keys())).all()
     assert (mdata.var.index == ['A', 'B', 'ITGB2', 'TNFRSF4']).all()
-    np.testing.assert_almost_equal(mdata.mod['metabolite'].X.mean(), -0.18889697, decimal=7)
-    np.testing.assert_almost_equal(mdata.mod['receptor'].X.mean(), 0.7754228, decimal=7)
+    np.testing.assert_almost_equal(mdata.mod['metabolite'].X.mean(), -0.18889697, decimal=5)
+    np.testing.assert_almost_equal(mdata.mod['receptor'].X.mean(), 0.7754228, decimal=5)