diff --git a/vllm/model_executor/models/adapters.py b/vllm/model_executor/models/adapters.py
index 32073cb88de4..6d035f93dd9b 100644
--- a/vllm/model_executor/models/adapters.py
+++ b/vllm/model_executor/models/adapters.py
@@ -399,6 +399,9 @@ def as_reward_model(cls: _T) -> _T:
     # 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
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 0d99597fa641..09e66a12d14f 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -3622,8 +3622,28 @@ def _dummy_pooler_run(
         hidden_states: torch.Tensor,
     ) -> PoolerOutput:
         # Find the task that has the largest output for subsequent steps
+        supported_pooling_tasks = self.get_supported_pooling_tasks()
+
+        if not supported_pooling_tasks:
+            if self.scheduler_config.chunked_prefill_enabled:
+                raise RuntimeError(
+                    f"Model {self.model_config.model} does not support "
+                    "any pooling tasks with chunked prefill enabled. "
+                    "Please add --no-enable-chunked-prefill to your "
+                    "config or CLI args. See "
+                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
+                    "to learn more."
+                )
+            else:
+                raise RuntimeError(
+                    f"Model {self.model_config.model} does not support "
+                    "any pooling tasks. See "
+                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
+                    "to learn more."
+                )
+
         output_size = dict[PoolingTask, float]()
-        for task in self.get_supported_pooling_tasks():
+        for task in supported_pooling_tasks:
             # Run a full batch with each task to ensure none of them OOMs
             output = self._dummy_pooler_run_task(hidden_states, task)
             output_size[task] = sum(o.nbytes for o in output)