diff --git a/tests/test_tensor/test_fitting.py b/tests/test_tensor/test_fitting.py
index 12295cc7..a509a233 100644
--- a/tests/test_tensor/test_fitting.py
+++ b/tests/test_tensor/test_fitting.py
@@ -12,13 +12,16 @@
 
 
 @pytest.mark.parametrize("method", ("auto", "dense", "overlap"))
-@pytest.mark.parametrize("normalized", (
-    True,
-    False,
-    "squared",
-    "infidelity",
-    "infidelity_sqrt",
-))
+@pytest.mark.parametrize(
+    "normalized",
+    (
+        True,
+        False,
+        "squared",
+        "infidelity",
+        "infidelity_sqrt",
+    ),
+)
 def test_tensor_network_distance(method, normalized):
     n = 6
     A = qtn.TN_rand_reg(n=n, reg=3, D=2, phys_dim=2, dtype=complex)
@@ -34,87 +37,100 @@ def test_tensor_network_distance(method, normalized):
 
 
 @pytest.mark.parametrize(
-    "method,opts",
-    (
-        ("als", (("enforce_pos", False), ("solver", "lstsq"))),
-        ("als", (("enforce_pos", True),)),
-        ("tree", ()),
+    "opts",
+    [
+        dict(method="als", dense_solve=False),
+        dict(method="als", dense_solve=False, solver="lgmres"),
+        dict(
+            method="als",
+            dense_solve=True,
+            enforce_pos=False,
+            solver_dense="lstsq",
+        ),
+        dict(method="als", dense_solve=True, enforce_pos=True),
+        dict(method="tree"),
         pytest.param(
-            "autodiff",
-            (("distance_method", "dense"),),
+            dict(method="autodiff", distance_method="dense"),
             marks=requires_autograd,
         ),
         pytest.param(
-            "autodiff",
-            (("distance_method", "overlap"),),
+            dict(method="autodiff", distance_method="overlap"),
             marks=requires_autograd,
         ),
-    ),
+    ],
 )
 @pytest.mark.parametrize("dtype", ("float64", "complex128"))
-def test_fit_mps(method, opts, dtype):
+def test_fit_mps(opts, dtype):
     k1 = qtn.MPS_rand_state(5, 3, seed=666, dtype=dtype)
     k2 = qtn.MPS_rand_state(5, 3, seed=667, dtype=dtype)
     assert k1.distance_normalized(k2) > 1e-3
-    k1.fit_(k2, method=method, progbar=True, **dict(opts))
+    k1.fit_(k2, progbar=True, **dict(opts))
     assert k1.distance_normalized(k2) < 1e-3
 
 
 @pytest.mark.parametrize(
-    "method,opts",
-    (
-        ("als", (("enforce_pos", False),)),
-        ("als", (("enforce_pos", True),)),
+    "opts",
+    [
+        dict(method="als", dense_solve=False),
+        dict(method="als", dense_solve=False, solver="lgmres"),
+        dict(
+            method="als",
+            dense_solve=True,
+            enforce_pos=False,
+            solver_dense="lstsq",
+        ),
+        dict(method="als", dense_solve=True, enforce_pos=True),
         pytest.param(
-            "autodiff",
-            (("distance_method", "dense"),),
+            dict(method="autodiff", distance_method="dense"),
             marks=requires_autograd,
         ),
         pytest.param(
-            "autodiff",
-            (("distance_method", "overlap"),),
+            dict(method="autodiff", distance_method="overlap"),
             marks=requires_autograd,
         ),
-    ),
+    ],
 )
 @pytest.mark.parametrize("dtype", ("float64", "complex128"))
-def test_fit_rand_reg(method, opts, dtype):
+def test_fit_rand_reg(opts, dtype):
     r1 = qtn.TN_rand_reg(5, 4, D=2, seed=666, phys_dim=2, dtype=dtype)
     k2 = qtn.MPS_rand_state(5, 3, seed=667, dtype=dtype)
     assert r1.distance(k2) > 1e-3
-    r1.fit_(k2, method=method, progbar=True, **dict(opts))
+    r1.fit_(k2, progbar=True, **dict(opts))
     assert r1.distance(k2) < 1e-3
 
 
 @pytest.mark.parametrize(
-    "method,opts",
-    (
-        ("als", (("enforce_pos", False),)),
-        ("als", (("enforce_pos", True),)),
-        ("tree", ()),
+    "opts",
+    [
+        dict(method="als", dense_solve=False),
+        dict(method="als", dense_solve=False, solver="lgmres"),
+        dict(
+            method="als",
+            dense_solve=True,
+            enforce_pos=False,
+            solver_dense="lstsq",
+        ),
+        dict(method="als", dense_solve=True, enforce_pos=True),
+        dict(method="tree"),
         pytest.param(
-            "autodiff",
-            (("distance_method", "dense"),),
+            dict(method="autodiff", distance_method="dense"),
             marks=requires_autograd,
         ),
         pytest.param(
-            "autodiff",
-            (("distance_method", "overlap"),),
+            dict(method="autodiff", distance_method="overlap"),
             marks=requires_autograd,
         ),
-    ),
+    ],
 )
 @pytest.mark.parametrize("dtype", ("float64", "complex128"))
-def test_fit_partial_tags(method, opts, dtype):
+def test_fit_partial_tags(opts, dtype):
     k1 = qtn.MPS_rand_state(5, 3, seed=666, dtype=dtype)
     k2 = qtn.MPS_rand_state(5, 3, seed=667, dtype=dtype)
     d0 = k1.distance(k2)
     tags = ["I0", "I2", "I4"]
-    k1f = k1.fit(
-        k2, tol=1e-3, tags=tags, method=method, progbar=True, **dict(opts)
-    )
+    k1f = k1.fit(k2, tol=1e-3, tags=tags, progbar=True, **dict(opts))
     assert k1f.distance(k2) < d0
-    if method != "tree":
+    if opts["method"] != "tree":
         assert (k1f[0] - k1[0]).norm() > 1e-12
         assert (k1f[1] - k1[1]).norm() < 1e-12
         assert (k1f[2] - k1[2]).norm() > 1e-12