diff --git a/ax/benchmark/problems/surrogate/lcbench/early_stopping.py b/ax/benchmark/problems/surrogate/lcbench/early_stopping.py
index 59b1dce3161..c2896989299 100644
--- a/ax/benchmark/problems/surrogate/lcbench/early_stopping.py
+++ b/ax/benchmark/problems/surrogate/lcbench/early_stopping.py
@@ -3,8 +3,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-# pyre-strict
-
 from collections.abc import Callable, Iterable, Mapping, Sequence
 from dataclasses import dataclass, field, InitVar
 from functools import partial
@@ -233,7 +231,9 @@ def __post_init__(
 
         parameter_names = get_lcbench_parameter_names()
         log_scale_parameter_names = get_lcbench_log_scale_parameter_names()
-        numeric_columns = set(parameter_names) - set(log_scale_parameter_names)
+        numeric_columns = [
+            name for name in parameter_names if name not in log_scale_parameter_names
+        ]
 
         self.metric_surrogate = _create_surrogate_regressor(
             base_regressor=metric_base_surrogate,
diff --git a/ax/benchmark/tests/problems/surrogate/lcbench/test_early_stopping.py b/ax/benchmark/tests/problems/surrogate/lcbench/test_early_stopping.py
index 552fc7f4dec..14963be2853 100644
--- a/ax/benchmark/tests/problems/surrogate/lcbench/test_early_stopping.py
+++ b/ax/benchmark/tests/problems/surrogate/lcbench/test_early_stopping.py
@@ -3,8 +3,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-# pyre-strict
-
 from unittest.mock import patch
 
 import numpy as np
@@ -62,6 +60,15 @@ def test_get_lcbench_early_stopping_problem(self) -> None:
             mock_create_surrogate_regressor.call_args_list
         )
         self.assertEqual(len(create_surrogate_regressor_call_args), 2)
+        for call in create_surrogate_regressor_call_args:
+            self.assertEqual(
+                call.kwargs["numeric_columns"],
+                ["max_dropout", "num_layers", "weight_decay"],
+            )
+            self.assertEqual(
+                call.kwargs["log_numeric_columns"],
+                ["batch_size", "max_units", "learning_rate", "momentum"],
+            )
         self.assertEqual(
             problem.optimization_config.objective.metric_names[0], DEFAULT_METRIC_NAME
         )