diff --git a/test/gromov/test_partial.py b/test/gromov/test_partial.py
index 249ec1d36..ce69385a1 100644
--- a/test/gromov/test_partial.py
+++ b/test/gromov/test_partial.py
@@ -94,8 +94,8 @@ def test_partial_gromov_wasserstein(nx):
             warn=True, verbose=True)
 
         resb_ = nx.to_numpy(resb)
-        np.testing.assert_allclose(res, 0, atol=1e-1, rtol=1e-1)
-        np.testing.assert_allclose(res, resb_, atol=1e-15)
+        np.testing.assert_allclose(res, 0, rtol=1e-4)
+        np.testing.assert_allclose(res, resb_, rtol=1e-4)
         assert np.all(res.sum(1) <= p)  # cf convergence wasserstein
         assert np.all(res.sum(0) <= q)  # cf convergence wasserstein
         np.testing.assert_allclose(
@@ -110,7 +110,7 @@ def test_partial_gromov_wasserstein(nx):
         C1b, C1subb, p=pb, q=psubb, m=m, log=True)
 
     resb_ = nx.to_numpy(resb)
-    np.testing.assert_allclose(res, resb_, atol=1e-15)
+    np.testing.assert_allclose(res, resb_, rtol=1e-4)
     assert np.all(res.sum(1) <= p)  # cf convergence wasserstein
     assert np.all(res.sum(0) <= psub)  # cf convergence wasserstein
     np.testing.assert_allclose(
@@ -123,8 +123,8 @@ def test_partial_gromov_wasserstein(nx):
     res0b, log0b = ot.gromov.partial_gromov_wasserstein(
         C1b, C2b, pb, qb, m=None, log=True)
     G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss')
-    np.testing.assert_allclose(G, res0, atol=1e-04)
-    np.testing.assert_allclose(res0b, res0, atol=1e-04)
+    np.testing.assert_allclose(G, res0, rtol=1e-4)
+    np.testing.assert_allclose(res0b, res0, rtol=1e-4)
 
     # tests for pGW2
     for loss_fun in ['square_loss', 'kl_loss']:
@@ -132,7 +132,7 @@ def test_partial_gromov_wasserstein(nx):
             C1, C2, p=None, q=q, m=m, loss_fun=loss_fun, log=True)
         w0_val = ot.gromov.partial_gromov_wasserstein2(
             C1b, C2b, p=pb, q=None, m=m, loss_fun=loss_fun, log=False)
-        np.testing.assert_allclose(w0, w0_val, rtol=1e-8)
+        np.testing.assert_allclose(w0, w0_val, rtol=1e-4)
 
     # tests integers
     C1_int = C1.astype(int)
@@ -199,7 +199,7 @@ def test_partial_partial_gromov_linesearch(nx):
         G0b, deltaGb, cost_G0b, df_G0b, fC1, fC2, hC1, hC2, 0., 1.,
         alpha_min=0., alpha_max=1.)
 
-    np.testing.assert_allclose(alpha, 1., atol=1e-2)
+    np.testing.assert_allclose(alpha, 1., rtol=1e-4)
 
 
 @pytest.skip_backend("jax", reason="test very slow with jax backend")
@@ -252,12 +252,12 @@ def test_entropic_partial_gromov_wasserstein(nx):
             symmetric=False, verbose=True)
 
         resb_ = nx.to_numpy(resb)
-        np.testing.assert_allclose(res, 0, atol=1e-1, rtol=1e-1)
-        np.testing.assert_allclose(res, resb_, atol=1e-15)
+        np.testing.assert_allclose(res, 0, rtol=1e-4)
+        np.testing.assert_allclose(res, resb_, rtol=1e-4)
         assert np.all(res.sum(1) <= p)  # cf convergence wasserstein
         assert np.all(res.sum(0) <= q)  # cf convergence wasserstein
         np.testing.assert_allclose(
-            np.sum(res), m, atol=1e-15)
+            np.sum(res), m, rtol=1e-4)
 
     # tests with m is None
     res = ot.gromov.entropic_partial_gromov_wasserstein(
@@ -272,7 +272,7 @@ def test_entropic_partial_gromov_wasserstein(nx):
     np.testing.assert_allclose(res, 0, atol=1e-1, rtol=1e-1)
     np.testing.assert_allclose(res, resb_, atol=1e-7)
     np.testing.assert_allclose(
-        np.sum(res), 1., atol=1e-8)
+        np.sum(res), 1., rtol=1e-4)
 
     # tests with different number of samples across spaces
     m = 0.5
@@ -283,11 +283,11 @@ def test_entropic_partial_gromov_wasserstein(nx):
         C1b, C1subb, p=pb, q=psubb, reg=1e4, m=m, log=True)
 
     resb_ = nx.to_numpy(resb)
-    np.testing.assert_allclose(res, resb_, atol=1e-15)
+    np.testing.assert_allclose(res, resb_, rtol=1e-4)
     assert np.all(res.sum(1) <= p)  # cf convergence wasserstein
     assert np.all(res.sum(0) <= psub)  # cf convergence wasserstein
     np.testing.assert_allclose(
-        np.sum(res), m, atol=1e-15)
+        np.sum(res), m, rtol=1e-4)
 
     # tests for pGW2
     for loss_fun in ['square_loss', 'kl_loss']: