diff --git a/ax/adapter/cross_validation.py b/ax/adapter/cross_validation.py
index e638367ffc1..45fa2cd9ade 100644
--- a/ax/adapter/cross_validation.py
+++ b/ax/adapter/cross_validation.py
@@ -240,12 +240,13 @@ def _efficient_loo_cross_validate(
         # This is a known limitation for fully Bayesian models and models with
         # non-Gaussian posteriors (such as PFNs).
         # Shape: n x 1 x m
-        loo_means = posterior.mixture_mean.detach().cpu().numpy()
-        loo_vars = posterior.mixture_variance.detach().cpu().numpy()
+        # Use .copy() to ensure arrays are writeable (numpy returns read-only views)
+        loo_means = posterior.mixture_mean.detach().cpu().numpy().copy()
+        loo_vars = posterior.mixture_variance.detach().cpu().numpy().copy()
     else:
         # Shape: n x 1 x m
-        loo_means = posterior.mean.detach().cpu().numpy()
-        loo_vars = posterior.variance.detach().cpu().numpy()
+        loo_means = posterior.mean.detach().cpu().numpy().copy()
+        loo_vars = posterior.variance.detach().cpu().numpy().copy()
 
     # Squeeze out the q dimension: n x 1 x m -> n x m
     loo_means = loo_means.squeeze(1)
diff --git a/ax/core/data.py b/ax/core/data.py
index 0ebd6fdc01a..56a89036611 100644
--- a/ax/core/data.py
+++ b/ax/core/data.py
@@ -195,9 +195,13 @@ def _safecast_df(cls: type[TData], df: pd.DataFrame) -> pd.DataFrame:
             if col in df.columns.values and coltype is not Any:
                 # Pandas timestamp handlng is weird
                 dtype = "datetime64[ns]" if coltype is pd.Timestamp else coltype
-                if (dtype != dtypes[col]) and not (
-                    coltype is int and df.loc[:, col].isnull().any()
-                ):
+                current_dtype = dtypes[col]
+                # Handle StringDtype -> object conversion (pandas 2.0+ compatibility)
+                needs_cast = (
+                    isinstance(current_dtype, pd.StringDtype)
+                    or (dtype != current_dtype)
+                ) and not (coltype is int and df.loc[:, col].isnull().any())
+                if needs_cast:
                     df[col] = df[col].astype(dtype)
         df.reset_index(inplace=True, drop=True)
         return df