Add validation to block unsupported parameters in post-training SFT, …

src/maxtext/trainers/post_train/dpo/train_dpo.py

@@ -182,6 +182,22 @@ def train(mt_config, goodput_recorder=None):
   return trainer, mesh
 
 
+def validate_config(config):
+  """Validates the configuration parameters for DPO training."""
+  if config.optimizer_memory_host_offload:
+    raise ValueError(
+        "optimizer_memory_host_offload=True is not supported on the post-training "
+        "DPO path because the underlying Tunix DPOTrainer does not support "
+        "host offloading of the optimizer state."
+    )
+
+  if config.num_vocab_tiling > 1:
+    raise ValueError(
+        f"Vocab Tiling is not supported with DPO. "
+        f"num_vocab_tiling was configured to {config.num_vocab_tiling}, but it must be 1 when running train_dpo."
+    )
+
+
 def main(argv: list[str]) -> None:
   """Main function to run DPO training.
 
@@ -191,6 +207,7 @@ def main(argv: list[str]) -> None:
   pathwaysutils.initialize()
 
   mt_config = pyconfig.initialize_pydantic(argv)
+  validate_config(mt_config)
   max_utils.print_system_information()
 
   goodput_recorder = create_goodput_recorder(mt_config)

src/maxtext/trainers/post_train/rl/train_rl.py

@@ -596,17 +596,28 @@ def rl_train(argv: Sequence[str], kwargs: dict):
     _rl_train_impl(argv, kwargs)
 
 
+def validate_config(config):
+  """Validates the configuration parameters for RL training."""
+  if config.optimizer_memory_host_offload:
+    raise ValueError(
+        "optimizer_memory_host_offload=True is not supported on the post-training "
+        "RL path because the underlying Tunix RLCluster/Trainer does not "
+        "support host offloading of the optimizer state."
+    )
+
+  if config.num_vocab_tiling > 1:
+    raise ValueError(
+        f"Vocab Tiling is not supported with RL. "
+        f"num_vocab_tiling was configured to {config.num_vocab_tiling}, but it must be 1 when running train_rl."
+    )
+
+
 def _rl_train_impl(argv: Sequence[str], kwargs: dict):
   """rl_train body — kept separate so _tpu_inference_compat_patches wraps it cleanly."""
   trainer_config, sampler_config, trainer_devices, sampler_devices = model_creation_utils.setup_configs_and_devices(
       argv, kwargs
   )
-
-  if trainer_config.num_vocab_tiling > 1:
-    raise ValueError(
-        f"Vocab Tiling is not supported with RL. "
-        f"num_vocab_tiling was configured to {trainer_config.num_vocab_tiling}, but it must be 1 when running train_rl."
-    )
+  validate_config(trainer_config)
 
   # Create model tokenizer first so we can plumb its pad_id into the model
   # adapter (used to synthesize segment_ids that mask pad positions from

src/maxtext/trainers/post_train/sft/train_sft.py

@@ -225,6 +225,16 @@ def loss_func(
   return trainer
 
 
+def validate_config(config):
+  """Validates the configuration parameters for SFT training."""
+  if config.optimizer_memory_host_offload:
+    raise ValueError(
+        "optimizer_memory_host_offload=True is not supported on the post-training "
+        "SFT path because the underlying Tunix PeftTrainer does not support "
+        "host offloading of the optimizer state."
+    )
+
+
 def setup_trainer_state(mt_config, goodput_recorder=None):
   """Set up prerequisites for training loop."""
   tunix_config = get_tunix_config(mt_config)
@@ -299,6 +309,7 @@ def main(argv: Sequence[str]) -> None:
   os.environ["TF_CPP_MIN_LOG_LEVEL"] = "0"
 
   mt_config = pyconfig.initialize_pydantic(argv)
+  validate_config(mt_config)
   max_utils.print_system_information()
 
   goodput_recorder = create_goodput_recorder(mt_config)

tests/post_training/unit/train_dpo_test.py

@@ -0,0 +1,58 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Unit tests for train_dpo.py."""
+
+import unittest
+from types import SimpleNamespace
+import pytest
+
+from maxtext.trainers.post_train.dpo import train_dpo
+
+pytestmark = [pytest.mark.post_training]
+
+
+class TrainDPOTest(unittest.TestCase):
+  """Tests for train_dpo.py."""
+
+  @pytest.mark.cpu_only
+  def test_validate_config_valid(self):
+    config = SimpleNamespace(
+        optimizer_memory_host_offload=False,
+        num_vocab_tiling=1,
+    )
+    # Should not raise any exception
+    train_dpo.validate_config(config)
+
+  @pytest.mark.cpu_only
+  def test_validate_config_invalid_offload(self):
+    config = SimpleNamespace(
+        optimizer_memory_host_offload=True,
+        num_vocab_tiling=1,
+    )
+    with self.assertRaisesRegex(ValueError, "optimizer_memory_host_offload=True is not supported"):
+      train_dpo.validate_config(config)
+
+  @pytest.mark.cpu_only
+  def test_validate_config_invalid_vocab_tiling(self):
+    config = SimpleNamespace(
+        optimizer_memory_host_offload=False,
+        num_vocab_tiling=2,
+    )
+    with self.assertRaisesRegex(ValueError, "Vocab Tiling is not supported with DPO"):
+      train_dpo.validate_config(config)
+
+
+if __name__ == "__main__":
+  unittest.main()

tests/post_training/unit/train_rl_test.py

@@ -476,11 +476,24 @@ def test_prepare_datasets_without_split(self, mock_load):
   def test_rl_train_invalid_vocab_tiling(self, mock_setup):
     mock_config = SimpleNamespace(
         num_vocab_tiling=2,
+        optimizer_memory_host_offload=False,
     )
     mock_setup.return_value = (mock_config, mock_config, [], [])
 
     with self.assertRaisesRegex(ValueError, "Vocab Tiling is not supported with RL"):
-      train_rl._rl_train_impl([], {})
+      train_rl._rl_train_impl([], {})  # pylint: disable=protected-access
+
+  @pytest.mark.cpu_only
+  @mock.patch("maxtext.trainers.post_train.rl.train_rl.model_creation_utils.setup_configs_and_devices")
+  def test_rl_train_invalid_optimizer_memory_host_offload(self, mock_setup):
+    mock_config = SimpleNamespace(
+        num_vocab_tiling=1,
+        optimizer_memory_host_offload=True,
+    )
+    mock_setup.return_value = (mock_config, mock_config, [], [])
+
+    with self.assertRaisesRegex(ValueError, "optimizer_memory_host_offload=True is not supported"):
+      train_rl._rl_train_impl([], {})  # pylint: disable=protected-access
 
 
 if __name__ == "__main__":

tests/post_training/unit/train_sft_test.py

@@ -0,0 +1,47 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Unit tests for train_sft.py."""
+
+import unittest
+from types import SimpleNamespace
+import pytest
+
+from maxtext.trainers.post_train.sft import train_sft
+
+pytestmark = [pytest.mark.post_training]
+
+
+class TrainSFTTest(unittest.TestCase):
+  """Tests for train_sft.py."""
+
+  @pytest.mark.cpu_only
+  def test_validate_config_valid(self):
+    config = SimpleNamespace(
+        optimizer_memory_host_offload=False,
+    )
+    # Should not raise any exception
+    train_sft.validate_config(config)
+
+  @pytest.mark.cpu_only
+  def test_validate_config_invalid_offload(self):
+    config = SimpleNamespace(
+        optimizer_memory_host_offload=True,
+    )
+    with self.assertRaisesRegex(ValueError, "optimizer_memory_host_offload=True is not supported"):
+      train_sft.validate_config(config)
+
+
+if __name__ == "__main__":
+  unittest.main()