diff --git a/tests/test_reward_trainer.py b/tests/test_reward_trainer.py
index 90be24348eb..c1bcd97b713 100644
--- a/tests/test_reward_trainer.py
+++ b/tests/test_reward_trainer.py
@@ -107,6 +107,18 @@ def test_collate_with_margin(self):
 
 
 class TestRewardTrainer(TrlTestCase):
+    def test_raises_error_when_model_num_labels_not_one(self):
+        """Test that RewardTrainer raises ValueError when model doesn't have num_labels=1."""
+        model = AutoModelForSequenceClassification.from_pretrained(
+            "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
+            dtype="float32",
+            # num_labels=2,  # Defaults to 2 num_labels for causal models
+        )
+
+        training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
+        with pytest.raises(ValueError, match=r"reward models require `num_labels=1`"):
+            RewardTrainer(model=model, args=training_args)
+
     @pytest.mark.parametrize(
         "model_id",
         [
@@ -176,6 +188,7 @@ def test_train_model(self):
         # Instantiate the model
         model = AutoModelForSequenceClassification.from_pretrained(
             "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
+            num_labels=1,  # required for reward models
             dtype="float32",
         )
 
@@ -341,7 +354,11 @@ def test_train_moe_with_peft_config(self):
     def test_train_peft_model(self):
         # Get the base model
         model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
-        model = AutoModelForSequenceClassification.from_pretrained(model_id, dtype="float32")
+        model = AutoModelForSequenceClassification.from_pretrained(
+            model_id,
+            num_labels=1,  # required for reward models
+            dtype="float32",
+        )
 
         # Get the base model parameter names
         base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
diff --git a/trl/trainer/reward_trainer.py b/trl/trainer/reward_trainer.py
index 2677901726b..9c309060e3c 100644
--- a/trl/trainer/reward_trainer.py
+++ b/trl/trainer/reward_trainer.py
@@ -354,6 +354,13 @@ def __init__(
                     "You passed `model_init_kwargs` to the `RewardConfig`, but your model is already instantiated. "
                     "The `model_init_kwargs` will be ignored."
                 )
+            # Validate that the model has num_labels = 1 (required for reward models)
+            if getattr(model.config, "num_labels", None) != 1:
+                raise ValueError(
+                    f"The model has `num_labels={model.config.num_labels}`, but reward models require `num_labels=1` "
+                    "to output a single scalar reward per sequence. Please instantiate your model with `num_labels=1` "
+                    "or pass a model name as a string to have it configured automatically."
+                )
 
         # Processing class
         if processing_class is None: