diff --git a/tests/unit/autogram/test_engine.py b/tests/unit/autogram/test_engine.py
index be7b593b..ba4ec06a 100644
--- a/tests/unit/autogram/test_engine.py
+++ b/tests/unit/autogram/test_engine.py
@@ -57,28 +57,7 @@
 )
 from utils.tensors import make_tensors
 
-from torchjd.aggregation import (
-    IMTLG,
-    MGDA,
-    Aggregator,
-    AlignedMTL,
-    AlignedMTLWeighting,
-    DualProj,
-    DualProjWeighting,
-    IMTLGWeighting,
-    Mean,
-    MeanWeighting,
-    MGDAWeighting,
-    PCGrad,
-    PCGradWeighting,
-    Random,
-    RandomWeighting,
-    Sum,
-    SumWeighting,
-    UPGrad,
-    UPGradWeighting,
-    Weighting,
-)
+from torchjd.aggregation import UPGrad, UPGradWeighting
 from torchjd.autogram._engine import Engine
 from torchjd.autojac._transform import Diagonalize, Init, Jac, OrderedSet
 from torchjd.autojac._transform._aggregate import _Matrixify
@@ -126,35 +105,11 @@
     param(InstanceNormMobileNetV2, 2, marks=[mark.slow, mark.garbage_collect]),
 ]
 
-AGGREGATORS_AND_WEIGHTINGS: list[tuple[Aggregator, Weighting]] = [
-    (UPGrad(), UPGradWeighting()),
-    (AlignedMTL(), AlignedMTLWeighting()),
-    (DualProj(), DualProjWeighting()),
-    (IMTLG(), IMTLGWeighting()),
-    (Mean(), MeanWeighting()),
-    (MGDA(), MGDAWeighting()),
-    (PCGrad(), PCGradWeighting()),
-    (Random(), RandomWeighting()),
-    (Sum(), SumWeighting()),
-]
-
-try:
-    from torchjd.aggregation import CAGrad, CAGradWeighting
-
-    AGGREGATORS_AND_WEIGHTINGS.append((CAGrad(c=0.5), CAGradWeighting(c=0.5)))
-except ImportError:
-    pass
-
-WEIGHTINGS = [weighting for _, weighting in AGGREGATORS_AND_WEIGHTINGS]
-
 
 @mark.parametrize(["architecture", "batch_size"], PARAMETRIZATIONS)
-@mark.parametrize(["aggregator", "weighting"], AGGREGATORS_AND_WEIGHTINGS)
 def test_equivalence_autojac_autogram(
     architecture: type[ShapedModule],
     batch_size: int,
-    aggregator: Aggregator,
-    weighting: Weighting,
 ):
     """
     Tests that the autogram engine gives the same results as the autojac engine on IWRM for several
@@ -166,6 +121,9 @@ def test_equivalence_autojac_autogram(
     input_shapes = architecture.INPUT_SHAPES
     output_shapes = architecture.OUTPUT_SHAPES
 
+    weighting = UPGradWeighting()
+    aggregator = UPGrad()
+
     torch.manual_seed(0)
     model_autojac = architecture().to(device=DEVICE)
     torch.manual_seed(0)
@@ -262,9 +220,8 @@ def _non_empty_subsets(elements: set) -> list[set]:
     return [set(c) for r in range(1, len(elements) + 1) for c in combinations(elements, r)]
 
 
-@mark.parametrize("weighting", WEIGHTINGS)
 @mark.parametrize("gramian_module_names", _non_empty_subsets({"fc0", "fc1", "fc2", "fc3", "fc4"}))
-def test_partial_autogram(weighting: Weighting, gramian_module_names: set[str]):
+def test_partial_autogram(gramian_module_names: set[str]):
     """
     Tests that partial JD via the autogram engine works similarly as if the gramian was computed via
     the autojac engine.
@@ -276,6 +233,8 @@ def test_partial_autogram(weighting: Weighting, gramian_module_names: set[str]):
     architecture = SimpleBranched
     batch_size = 64
 
+    weighting = UPGradWeighting()
+
     input_shapes = architecture.INPUT_SHAPES
     output_shapes = architecture.OUTPUT_SHAPES