diff --git a/tests/models/test_registry.py b/tests/models/test_registry.py
index 9017a0fd9140..a089696e10ff 100644
--- a/tests/models/test_registry.py
+++ b/tests/models/test_registry.py
@@ -13,7 +13,6 @@
 )
 from vllm.model_executor.models.adapters import (
     as_embedding_model,
-    as_reward_model,
     as_seq_cls_model,
 )
 from vllm.model_executor.models.registry import (
@@ -46,7 +45,6 @@ def test_registry_imports(model_arch):
     # All vLLM models should be convertible to a pooling model
     assert is_pooling_model(as_seq_cls_model(model_cls))
     assert is_pooling_model(as_embedding_model(model_cls))
-    assert is_pooling_model(as_reward_model(model_cls))
 
     if model_arch in _MULTIMODAL_MODELS:
         assert supports_multimodal(model_cls)
diff --git a/vllm/model_executor/model_loader/utils.py b/vllm/model_executor/model_loader/utils.py
index c68ac611558a..c76a2da7526c 100644
--- a/vllm/model_executor/model_loader/utils.py
+++ b/vllm/model_executor/model_loader/utils.py
@@ -22,7 +22,6 @@
 )
 from vllm.model_executor.models.adapters import (
     as_embedding_model,
-    as_reward_model,
     as_seq_cls_model,
     try_create_mm_pooling_model_cls,
 )
@@ -209,9 +208,6 @@ def _get_model_architecture(model_config: ModelConfig) -> tuple[type[nn.Module],
     elif convert_type == "classify":
         logger.debug_once("Converting to sequence classification model.")
         model_cls = as_seq_cls_model(model_cls)
-    elif convert_type == "reward":
-        logger.debug_once("Converting to reward model.")
-        model_cls = as_reward_model(model_cls)
     else:
         assert_never(convert_type)
 
diff --git a/vllm/model_executor/models/adapters.py b/vllm/model_executor/models/adapters.py
index 6d035f93dd9b..1d101156a6df 100644
--- a/vllm/model_executor/models/adapters.py
+++ b/vllm/model_executor/models/adapters.py
@@ -382,40 +382,6 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
     return ModelForSequenceClassification  # type: ignore
 
 
-def as_reward_model(cls: _T) -> _T:
-    """
-    Subclass an existing vLLM model to support reward modeling.
-
-    By default, we return the hidden states of each token directly.
-
-    Note:
-        We assume that no extra layers are added to the original model;
-        please implement your own model if this is not the case.
-    """
-    # Avoid modifying existing reward models
-    if is_pooling_model(cls):
-        return cls
-
-    # Lazy import
-    from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
-
-    from .interfaces_base import default_pooling_type
-
-    @default_pooling_type("ALL")
-    class ModelForReward(_create_pooling_model_cls(cls)):
-        def _init_pooler(self, vllm_config: "VllmConfig", prefix: str = ""):
-            pooler_config = vllm_config.model_config.pooler_config
-            assert pooler_config is not None
-
-            self.pooler = DispatchPooler(
-                {"encode": Pooler.for_encode(pooler_config)},
-            )
-
-    ModelForReward.__name__ = _get_pooling_model_name(cls.__name__, "ForReward")
-
-    return ModelForReward  # type: ignore
-
-
 class SequenceClassificationConfig(VerifyAndUpdateConfig):
     @staticmethod
     def verify_and_update_config(vllm_config: "VllmConfig") -> None: