diff --git a/tests/test_dual_variables.py b/tests/test_dual_variables.py
index 5a0b0a2..c4da32d 100644
--- a/tests/test_dual_variables.py
+++ b/tests/test_dual_variables.py
@@ -250,7 +250,7 @@ def test_dual_gradcheck_inequality():
 
     # Function that returns dual variable for gradcheck
     def f(c_t):
-        x_opt, ineq_dual = layer(c_t)
+        x_opt, ineq_dual = layer(c_t, solver_args={"eps": 1e-10})
         return ineq_dual
 
     c_t = torch.tensor([1.0, -1.0], requires_grad=True)
@@ -303,7 +303,7 @@ def test_dual_gradcheck_vector_equality():
     )
 
     def f(A_t, b_t):
-        x_opt, eq_dual = layer(A_t, b_t)
+        x_opt, eq_dual = layer(A_t, b_t, solver_args={"eps": 1e-10})
         return eq_dual.sum()
 
     torch.manual_seed(42)
diff --git a/tests/test_torch.py b/tests/test_torch.py
index 44001eb..7459835 100644
--- a/tests/test_torch.py
+++ b/tests/test_torch.py
@@ -245,7 +245,10 @@ def test_sdp():
     # Use a well-conditioned symmetric matrix
     C_t = torch.tensor([[2.0, 0.5, 0.1], [0.5, 3.0, 0.2], [0.1, 0.2, 1.5]], requires_grad=True)
 
-    torch.autograd.gradcheck(layer, (C_t,), atol=1e-4, rtol=1e-3)
+    torch.autograd.gradcheck(
+        lambda C: layer(C, solver_args={"eps": 1e-10}),
+        (C_t,), atol=1e-4, rtol=1e-3,
+    )
 
 
 def test_not_enough_parameters():