diff --git a/tests/core/models/test_equiformer_v2.py b/tests/core/models/test_equiformer_v2.py
index f3c8870a2..54d58db1c 100644
--- a/tests/core/models/test_equiformer_v2.py
+++ b/tests/core/models/test_equiformer_v2.py
@@ -242,7 +242,7 @@ def _load_hydra_model():
     model.backbone.num_layers = 1
     return model
 
-def test_eqv2_hydra():
+def test_eqv2_hydra_activation_checkpoint():
     atoms = read(
         os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"),
         index=0,
@@ -260,17 +260,18 @@ def test_eqv2_hydra():
     ac_model = _load_hydra_model()
     ac_model.backbone.activation_checkpoint=True
 
+    # to do this test we need both models to have the exact same state and the only
+    # way to do this is save the rng state and reset it after stepping the first model
     start_rng_state = torch.random.get_rng_state()
     outputs_no_ac = no_ac_model(inputs)
-    print(outputs_no_ac)
     torch.autograd.backward(outputs_no_ac["energy"].sum() + outputs_no_ac["forces"].sum())
 
     # reset the rng state to the beginning
     torch.random.set_rng_state(start_rng_state)
     outptuts_ac = ac_model(inputs)
-    print(outptuts_ac)
     torch.autograd.backward(outptuts_ac["energy"].sum() + outptuts_ac["forces"].sum())
 
+    # assert all the gradients are identical between the model with checkpointing and no checkpointing
     ac_model_grad_dict = {name:p.grad for name, p in ac_model.named_parameters() if p.grad is not None}
     no_ac_model_grad_dict = {name:p.grad for name, p in no_ac_model.named_parameters() if p.grad is not None}
     for name in no_ac_model_grad_dict: