diff --git a/src/unilab/training/tensor_diagnostics.py b/src/unilab/training/tensor_diagnostics.py
index 080ebb324..9e19c7579 100644
--- a/src/unilab/training/tensor_diagnostics.py
+++ b/src/unilab/training/tensor_diagnostics.py
@@ -128,10 +128,18 @@ def capture_due(self, step: int) -> bool:
         return self._cfg.enabled and interval > 0 and step % interval == 0
 
     def reset_verdict(self) -> None:
-        if self._device_bad_rows is not None:
-            self._device_bad_rows.zero_()
-        if self._device_verdict is not None:
-            self._device_verdict.zero_()
+        # The device latches may be inference tensors: rsl_rl wraps the whole
+        # rollout in torch.inference_mode(), so with deferred validation the
+        # buffers are typically first allocated inside that context. In-place
+        # zeroing of an inference tensor is only legal under inference mode,
+        # and poll sites (cycle boundaries, close) run outside it, so run the
+        # reset under inference mode explicitly; it is a no-op on the
+        # autograd bookkeeping either way.
+        with torch.inference_mode():
+            if self._device_bad_rows is not None:
+                self._device_bad_rows.zero_()
+            if self._device_verdict is not None:
+                self._device_verdict.zero_()
 
     def _validate_finite_float(self, value: torch.Tensor, label: str) -> None:
         if not isinstance(value, torch.Tensor):
diff --git a/tests/training/test_tensor_diagnostics.py b/tests/training/test_tensor_diagnostics.py
index e19fc83b3..87a2995ab 100644
--- a/tests/training/test_tensor_diagnostics.py
+++ b/tests/training/test_tensor_diagnostics.py
@@ -200,3 +200,20 @@ def test_training_routes_guard_and_shared_finite_boundary() -> None:
     cfg.training.finite_validation_interval = 0
     with pytest.raises(ValueError, match="positive integer"):
         finite_validation_env_overrides(cfg)
+
+
+def test_reset_verdict_allows_inference_tensor_latches(tmp_path: Path) -> None:
+    """Latches allocated inside an rsl_rl rollout must survive an outside reset.
+
+    rsl_rl wraps the whole rollout in ``torch.inference_mode()``; with
+    deferred validation the guard's device buffers are typically first
+    allocated there, and ``close()`` resets them outside the context. The
+    reset must not fail on inference tensors.
+    """
+    guard = _guard(tmp_path)
+    obs = {"obs": torch.zeros((3, 2))}
+    reward = torch.zeros(3)
+    with torch.inference_mode():
+        assert guard.check(obs, reward, defer=True) is None
+    assert guard.device_verdict is not None
+    guard.reset_verdict()