diff --git a/src/olmo_core/distributed/checkpoint.py b/src/olmo_core/distributed/checkpoint.py
index b4f75ca7..16315553 100644
--- a/src/olmo_core/distributed/checkpoint.py
+++ b/src/olmo_core/distributed/checkpoint.py
@@ -360,15 +360,6 @@ def load(
         metadata = metadata or self.get_metadata(dir, no_dist=no_dist)
         safetensors_mfl = _safetensors_mfl or SafeTensorsMultiFileLoader()
 
-        def validate_shard_in_file(tensor: torch.Tensor, loader: SafeTensorsLoader, key: str, filename: str):
-            if len((shape_in_file := loader.get_shape(key))) != 1:
-                raise ValueError(f"Expected a 1D tensor at {key} in {filename}, found shape {shape_in_file}")
-
-            if (dtype := loader.get_dtype(key)) != tensor.dtype:
-                raise ValueError(
-                    f"Data type mismatch between tensor to load ({dtype}) and to load into ({tensor.dtype})"
-                )
-
         # Load each tensor from the slices in each file.
         for key in state_dict.keys():
             log.debug("Loading tensor '%s' from state dict...", key)
@@ -395,7 +386,15 @@ def validate_shard_in_file(tensor: torch.Tensor, loader: SafeTensorsLoader, key:
                     continue  # no overlap with data in file, so nothing to load
 
                 with safetensors_mfl.open(f"{dir}/{filename}") as loader:
-                    validate_shard_in_file(tensor, loader, key, filename)
+                    # Validate the shard in the file.
+                    if len((shape_in_file := loader.get_shape(key))) != 1:
+                        raise ValueError(
+                            f"Expected a 1D tensor at {key} in {filename}, found shape {shape_in_file}"
+                        )
+                    if (dtype := loader.get_dtype(key)) != tensor.dtype:
+                        raise ValueError(
+                            f"Data type mismatch between tensor to load ({dtype}) and to load into ({tensor.dtype})"
+                        )
 
                     if overlap == OverlapType.EQUAL:
                         flat_view.view.copy_(loader.get_flat_slice(key))
@@ -826,7 +825,7 @@ def compute_overlap_with(self, other: TensorShardSpec, full_shape: Tuple[int, ..
             else:
                 return OverlapType.MIXED
 
-        return None
+        return OverlapType.MIXED
 
 
 def _offsets_overlap(offsets: Tuple[int, int], other_offsets: Tuple[int, int]) -> bool: