Fix: Add validation to bedrock reward models - RLAIF Trainer

sagemaker-train/src/sagemaker/train/constants.py

@@ -41,3 +41,11 @@
 ]
 
 HUB_NAME = "SageMakerPublicHub"
+
+# Allowed reward model IDs for RLAIF trainer
+_ALLOWED_REWARD_MODEL_IDS = [
+    "openai.gpt-oss-120b-1:0",
+    "openai.gpt-oss-20b-1:0", 
+    "qwen.qwen3-32b-v1:0",
+    "qwen.qwen3-coder-30b-a3b-v1:0"
+]

sagemaker-train/src/sagemaker/train/rlaif_trainer.py

@@ -26,7 +26,7 @@
 )
 from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter
 from sagemaker.core.telemetry.constants import Feature
-from sagemaker.train.constants import HUB_NAME
+from sagemaker.train.constants import HUB_NAME, _ALLOWED_REWARD_MODEL_IDS
 
 logger = logging.getLogger(__name__)
 
@@ -87,7 +87,6 @@ class RLAIFTrainer(BaseTrainer):
             ARN, or ModelPackageGroup object. Required when model is not a ModelPackage.
         reward_model_id (str):
             Bedrock model identifier for generating LLM feedback.
-            Evaluator models available: https://docs.aws.amazon.com/bedrock/latest/userguide/evaluation-judge.html
             Required for RLAIF training to provide reward signals.
         reward_prompt (Union[str, Evaluator]):
             The reward prompt or evaluator for AI feedback generation.
@@ -141,7 +140,7 @@ def __init__(
         self.training_type = training_type
         self.model_package_group_name = _validate_and_resolve_model_package_group(model,
                                                                                  model_package_group_name)
-        self.reward_model_id = reward_model_id
+        self.reward_model_id = self._validate_reward_model_id(reward_model_id)
         self.reward_prompt = reward_prompt
         self.mlflow_resource_arn = mlflow_resource_arn
         self.mlflow_experiment_name = mlflow_experiment_name
@@ -165,6 +164,18 @@ def __init__(
 
         # Process reward_prompt parameter
         self._process_hyperparameters()
+
+    def _validate_reward_model_id(self, reward_model_id):
+        """Validate reward_model_id is one of the allowed values."""
+        if not reward_model_id:
+            return None
+
+        if reward_model_id not in _ALLOWED_REWARD_MODEL_IDS:
+            raise ValueError(
+                f"Invalid reward_model_id '{reward_model_id}'. "
+                f"Available models are: {_ALLOWED_REWARD_MODEL_IDS}"
+            )
+        return reward_model_id
 
 
     @_telemetry_emitter(feature=Feature.MODEL_CUSTOMIZATION, func_name="RLAIFTrainer.train")

sagemaker-train/tests/unit/train/test_rlaif_trainer.py

@@ -503,3 +503,32 @@ def test_process_non_builtin_reward_prompt_hub_content_error(self):
 
             with pytest.raises(ValueError, match="Custom prompt 'invalid-prompt' not found in HubContent"):
                 trainer._process_non_builtin_reward_prompt()
+
+    def test_validate_reward_model_id_valid_models(self):
+        """Test _validate_reward_model_id with valid model IDs."""
+        trainer = RLAIFTrainer.__new__(RLAIFTrainer)
+
+        valid_models = [
+            "openai.gpt-oss-120b-1:0",
+            "openai.gpt-oss-20b-1:0", 
+            "qwen.qwen3-32b-v1:0",
+            "qwen.qwen3-coder-30b-a3b-v1:0"
+        ]
+
+        for model_id in valid_models:
+            result = trainer._validate_reward_model_id(model_id)
+            assert result == model_id
+
+    def test_validate_reward_model_id_invalid_model(self):
+        """Test _validate_reward_model_id raises error for invalid model ID."""
+        trainer = RLAIFTrainer.__new__(RLAIFTrainer)
+
+        with pytest.raises(ValueError, match="Invalid reward_model_id 'invalid-model-id'"):
+            trainer._validate_reward_model_id("invalid-model-id")
+
+    def test_validate_reward_model_id_none_model(self):
+        """Test _validate_reward_model_id handles None model ID."""
+        trainer = RLAIFTrainer.__new__(RLAIFTrainer)
+
+        result = trainer._validate_reward_model_id(None)
+        assert result is None