diff --git a/test/test_specs.py b/test/test_specs.py
index 058144c1a94..c8b780e49a1 100644
--- a/test/test_specs.py
+++ b/test/test_specs.py
@@ -3404,6 +3404,14 @@ def test_project(self, shape, device, spectype, rand_shape, n=5):
             assert (sp != s).all()
 
 
+class TestNonTensorSpec:
+    def test_sample(self):
+        nts = NonTensorSpec(shape=(3, 4))
+        assert nts.one((2,)).shape == (2, 3, 4)
+        assert nts.rand((2,)).shape == (2, 3, 4)
+        assert nts.zero((2,)).shape == (2, 3, 4)
+
+
 if __name__ == "__main__":
     args, unknown = argparse.ArgumentParser().parse_known_args()
     pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)
diff --git a/torchrl/data/tensor_specs.py b/torchrl/data/tensor_specs.py
index 50999eb5f36..fc8e64e10a8 100644
--- a/torchrl/data/tensor_specs.py
+++ b/torchrl/data/tensor_specs.py
@@ -1949,14 +1949,26 @@ def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> NonTensorSpec:
     def clone(self) -> NonTensorSpec:
         return self.__class__(shape=self.shape, device=self.device, dtype=self.dtype)
 
-    def rand(self, shape):
-        return NonTensorData(data=None, batch_size=self.shape, device=self.device)
+    def rand(self, shape=None):
+        if shape is None:
+            shape = ()
+        return NonTensorData(
+            data=None, batch_size=(*shape, self.shape), device=self.device
+        )
 
-    def zero(self, batch_size):
-        return NonTensorData(data=None, batch_size=self.shape, device=self.device)
+    def zero(self, shape=None):
+        if shape is None:
+            shape = ()
+        return NonTensorData(
+            data=None, batch_size=(*shape, self.shape), device=self.device
+        )
 
-    def one(self, batch_size):
-        return NonTensorData(data=None, batch_size=self.shape, device=self.device)
+    def one(self, shape=None):
+        if shape is None:
+            shape = ()
+        return NonTensorData(
+            data=None, batch_size=(*shape, self.shape), device=self.device
+        )
 
     def is_in(self, val: torch.Tensor) -> bool:
         shape = torch.broadcast_shapes(self.shape, val.shape)