diff --git a/torchrl/data/tensor_specs.py b/torchrl/data/tensor_specs.py
index efe928856b9..3dd95a1284b 100644
--- a/torchrl/data/tensor_specs.py
+++ b/torchrl/data/tensor_specs.py
@@ -1456,7 +1456,11 @@ def is_in(self, val: torch.Tensor) -> bool:
         shape = torch.broadcast_shapes(shape, val.shape)
         mask_expand = self.mask.expand(shape)
         gathered = mask_expand & val
-        return gathered.any(-1).all()
+        return (
+            (gathered.sum(-1) == 1).all()
+            and val.dtype == self.dtype
+            and val.shape[-len(self.shape) :] == self.shape
+        )
 
     def __eq__(self, other):
         if not hasattr(other, "mask"):
@@ -1763,9 +1767,12 @@ def _project(self, val: torch.Tensor) -> torch.Tensor:
 
     def is_in(self, val: torch.Tensor) -> bool:
         try:
-            return (val >= self.space.low.to(val.device)).all() and (
-                val <= self.space.high.to(val.device)
-            ).all()
+            return (
+                (val >= self.space.low.to(val.device)).all()
+                and (val <= self.space.high.to(val.device)).all()
+                and (val.dtype == self.dtype)
+                and (val.shape[-len(self.shape) :] == self.shape)
+            )
         except RuntimeError as err:
             if "The size of tensor a" in str(err):
                 warnings.warn(f"Got a shape mismatch: {str(err)}")
@@ -1894,7 +1901,7 @@ def rand(self, shape=None) -> torch.Tensor:
         return torch.empty(shape, device=self.device, dtype=self.dtype).random_()
 
     def is_in(self, val: torch.Tensor) -> bool:
-        return True
+        return val.dtype == self.dtype and val.shape[-len(self.shape) :] == self.shape
 
     def expand(self, *shape):
         if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
@@ -2034,7 +2041,7 @@ def rand(self, shape=None) -> torch.Tensor:
         return r.to(self.device)
 
     def is_in(self, val: torch.Tensor) -> bool:
-        return True
+        return val.dtype == self.dtype and val.shape[-len(self.shape) :] == self.shape
 
     def expand(self, *shape):
         if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
@@ -2589,7 +2596,11 @@ def is_in(self, val: torch.Tensor) -> bool:
         shape = torch.Size([*torch.broadcast_shapes(shape[:-1], val.shape), shape[-1]])
         mask_expand = self.mask.expand(shape)
         gathered = mask_expand.gather(-1, val.unsqueeze(-1))
-        return gathered.all()
+        return (
+            gathered.all()
+            and (val.dtype == self.dtype)
+            and (val.shape[-len(self.shape) :] == self.shape)
+        )
 
     def __getitem__(self, idx: SHAPE_INDEX_TYPING):
         """Indexes the current TensorSpec based on the provided index."""