diff --git a/simpletuner/helpers/training/trainer.py b/simpletuner/helpers/training/trainer.py
index 483cd4d68..cbdc7c7dd 100644
--- a/simpletuner/helpers/training/trainer.py
+++ b/simpletuner/helpers/training/trainer.py
@@ -598,7 +598,10 @@ def _update_grad_metrics(
         elif (
             not require_value_method or self.config.grad_clip_method == "value"
         ) and not self.config.use_deepspeed_optimizer:
-            target_logs[f"{prefix}grad_absmax"] = self.grad_norm
+            grad_value = self.grad_norm
+            if clone_norm_value:
+                grad_value = float(self.grad_norm.clone().detach())
+            target_logs[f"{prefix}grad_absmax"] = grad_value
 
     def _config_uses_bitsandbytes(self) -> bool:
         if not getattr(self, "config", None):
diff --git a/tests/test_trainer.py b/tests/test_trainer.py
index e2a93cd77..7f741bf88 100644
--- a/tests/test_trainer.py
+++ b/tests/test_trainer.py
@@ -1249,6 +1249,19 @@ def test_update_grad_metrics_logs_absmax_without_deepspeed(self):
         self.assertIn("grad_absmax", logs)
         self.assertIs(logs["grad_absmax"], trainer.grad_norm)
 
+    def test_update_grad_metrics_clones_absmax_value_when_requested(self):
+        trainer = self._build_trainer_for_grad_logging(
+            grad_clip_method="value",
+            use_deepspeed=False,
+            grad_value=torch.tensor(1.2),
+        )
+        logs = {}
+        trainer._update_grad_metrics(logs, clone_norm_value=True)
+        self.assertIn("grad_absmax", logs)
+        self.assertEqual(
+            logs["grad_absmax"], float(trainer.grad_norm.clone().detach())
+        )
+
     def test_update_grad_metrics_clones_norm_value_when_requested(self):
         trainer = self._build_trainer_for_grad_logging(
             grad_clip_method="norm",
@@ -1309,7 +1322,9 @@ def test_compose_training_progress_metrics_includes_grad_absmax(self):
         )
         metrics = trainer._compose_training_progress_metrics(epoch=1)
         self.assertIn("grad_absmax", metrics)
-        self.assertIs(metrics["grad_absmax"], trainer.grad_norm)
+        self.assertEqual(
+            metrics["grad_absmax"], float(trainer.grad_norm.clone().detach())
+        )
         self.assertNotIn("grad_norm", metrics)
 
     def test_compose_training_progress_metrics_excludes_grad_with_deepspeed(self):