diff --git a/src/art/megatron/lora.py b/src/art/megatron/lora.py
index f3baead14..6d0e32644 100644
--- a/src/art/megatron/lora.py
+++ b/src/art/megatron/lora.py
@@ -1694,6 +1694,7 @@ def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]:
         )
         alpha = beta.clone()
         adapter_output = torch.cat([qkv, z, beta, alpha], dim=-1)
+        del qkv, z, beta, alpha
         return linear_output + adapter_output, bias
 
 
diff --git a/tests/integration/megatron/gdn_shared_prefix/test_real_gdn_tp_lora.py b/tests/integration/megatron/gdn_shared_prefix/test_real_gdn_tp_lora.py
index 0314fd681..191ff5056 100644
--- a/tests/integration/megatron/gdn_shared_prefix/test_real_gdn_tp_lora.py
+++ b/tests/integration/megatron/gdn_shared_prefix/test_real_gdn_tp_lora.py
@@ -19,6 +19,8 @@
 )
 from torch.distributed import destroy_process_group, init_process_group  # noqa: E402
 import torch.multiprocessing as mp  # noqa: E402
+from torch.multiprocessing.reductions import StorageWeakRef  # noqa: E402
+from torch.utils._python_dispatch import TorchDispatchMode  # noqa: E402
 
 from art.megatron.lora import apply_lora_adapters  # noqa: E402
 from art.megatron.model_support import QWEN3_5_MOE_SPEC  # noqa: E402
@@ -63,6 +65,70 @@ def test_real_qwen35_gdn_lora_gradients_match_flattened() -> None:
         assert _gdn_lora_grad_names(packed_gdn)
 
 
+class _AdapterOutputLifetime(TorchDispatchMode):
+    """Observe storage ownership without retaining projection tensors."""
+
+    def __init__(self):
+        super().__init__()
+        self.parts = ()
+        self.adapter = None
+        self.at_add = []
+
+    def __torch_dispatch__(self, func, types, args: tuple = (), kwargs=None):
+        if (
+            func is torch.ops.aten.add.Tensor
+            and self.adapter is not None
+            and not self.adapter.expired()
+            and args[1].untyped_storage()._cdata == self.adapter.cdata
+        ):
+            self.at_add.append(tuple(part.expired() for part in self.parts))
+        output = func(*args, **(kwargs or {}))
+        if func is torch.ops.aten.cat.default and len(args[0]) == 4:
+            self.parts = tuple(StorageWeakRef(t.untyped_storage()) for t in args[0])
+            self.adapter = StorageWeakRef(output.untyped_storage())
+        return output
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available(),
+    reason="CUDA is required for real Megatron/FLA GDN LoRA coverage.",
+)
+@pytest.mark.parametrize("grad_enabled", (False, True), ids=("no_grad", "grad"))
+def test_real_qwen35_gdn_lora_releases_components_before_add(
+    grad_enabled: bool,
+) -> None:
+    with _single_rank_model_parallel(), torch.set_grad_enabled(grad_enabled):
+        gdn, _ = _make_matching_gdn_pair(tp_size=1, lora=True)
+        projection = gdn.in_proj
+        x = torch.randn(
+            16,
+            1,
+            64,
+            device="cuda",
+            dtype=GDN_CORRECTNESS_DTYPE,
+            requires_grad=grad_enabled,
+        )
+        with _AdapterOutputLifetime() as lifetime:
+            output, bias = projection(x)
+        # One observed add is required; an empty observation must fail.
+        assert lifetime.at_add == [(True, True, True, True)]
+        assert bias is None
+        assert torch.isfinite(output).all()
+        assert output.requires_grad == grad_enabled
+        if grad_enabled:
+            output.square().mean().backward()
+            gradients = [
+                parameter.grad
+                for lora in (projection.qkv_lora, projection.z_lora)
+                for parameter in (lora.A_T, lora.B_T)
+            ]
+            assert len(gradients) == 4
+            for gradient in [x.grad, *gradients]:
+                assert gradient is not None
+                assert torch.isfinite(gradient).all()
+                assert torch.count_nonzero(gradient) > 0
+
+
 @pytest.mark.skipif(
     not torch.cuda.is_available() or torch.cuda.device_count() < 2,
     reason="At least two CUDA devices are required for TP2 GDN coverage.",