diff --git a/test/llm/test_llm_transforms.py b/test/llm/test_llm_transforms.py
index b6d0c1a6853..5f3f11133fd 100644
--- a/test/llm/test_llm_transforms.py
+++ b/test/llm/test_llm_transforms.py
@@ -18,6 +18,7 @@
     ExecuteToolsInOrder,
     IncrementalTokenizer,
     JSONCallParser,
+    PolicyVersion,
     ToolCall,
     ToolRegistry,
     XMLBlockParser,
@@ -718,3 +719,31 @@ def test_empty_history_handling(self, tokenizer):
         assert ("tokens", "prompt") in result.keys(True, True)
         tokens = result.get(("tokens", "prompt"), as_list=True)
         assert tokens[0].numel() > 0
+
+
+class TestPolicyVersion:
+    def test_int_version_dtype_and_device(self):
+        """Integer policy version must stay int64 and follow the tensordict device.
+
+        Regression for a bug where ``version_type="int"`` was cast to float
+        and dropped the device, producing CPU float tensors that mismatched
+        the surrounding tensordict.
+        """
+        import torch
+
+        transform = PolicyVersion(version_type="int")
+        transform.version = 7
+
+        td = TensorDict(batch_size=(4,))
+        out = transform._step(td, td.copy())
+        version = out.get("policy_version")
+        assert version.dtype == torch.int64
+        assert version.shape == (4,)
+        assert torch.equal(version, torch.full((4,), 7, dtype=torch.int64))
+
+        if torch.cuda.is_available():
+            td_cuda = TensorDict(batch_size=(4,), device="cuda")
+            out_cuda = transform._step(td_cuda, td_cuda.copy())
+            version_cuda = out_cuda.get("policy_version")
+            assert version_cuda.dtype == torch.int64
+            assert version_cuda.device.type == "cuda"
diff --git a/torchrl/envs/llm/transforms/policy_version.py b/torchrl/envs/llm/transforms/policy_version.py
index 493b630780c..430ce515b58 100644
--- a/torchrl/envs/llm/transforms/policy_version.py
+++ b/torchrl/envs/llm/transforms/policy_version.py
@@ -159,8 +159,15 @@ def _step(
         if self.version_type in (str, "uuid"):
             version = NonTensorData(self.version).expand(next_tensordict.shape)
         elif self.version_type in (int, "int"):
-            # Cast to float for torch.full
-            version = torch.full(next_tensordict.shape, float(cast(int, self.version)))
+            device = next_tensordict.device
+            kwargs = {"dtype": torch.int64}
+            if device is not None:
+                kwargs["device"] = device
+            version = torch.full(
+                next_tensordict.shape,
+                cast(int, self.version),
+                **kwargs,
+            )
         else:
             raise ValueError(f"Invalid version type: {self.version_type}")