diff --git a/applications/ColossalChat/coati/distributed/consumer.py b/applications/ColossalChat/coati/distributed/consumer.py
index 026056783..6f8f1b497 100644
--- a/applications/ColossalChat/coati/distributed/consumer.py
+++ b/applications/ColossalChat/coati/distributed/consumer.py
@@ -117,14 +117,12 @@ class BaseConsumer:
                         # receive data from producers
                         for r in range(self.num_producers):
                             print(f"[T{dist.get_rank()}] Recv data episode {episode} step {step} from {r}")
-                            raw_batch = unbind_batch(
-                                ray_broadcast_tensor_dict(None, src=0, device=self.device, group_name=f"sync_data_{r}")
-                            )
+                            raw_batch = ray_broadcast_tensor_dict(None, src=0, device=self.device, group_name=f"sync_data_{r}")
                             recv_effective_count = 0
                             # calculate group reward et al. filtering. As only the filtered group will be used for training (which is incomplete),
                             # we need to calculate the metrics before filtering here for logging
-                            for group in raw_batch:
-                                group_with_reward = self.calculate_group_reward(group)
+                            raw_batch_with_reward = unbind_batch(self.calculate_reward(raw_batch))
+                            for group_with_reward in raw_batch_with_reward:
                                 group_reward_mean = group_with_reward["reward"].mean().cpu().item()
                                 group_format_acc_mean = group_with_reward["format_acc"].mean().cpu().item()
                                 group_ans_acc_mean = group_with_reward["ans_acc"].mean().cpu().item()
@@ -139,7 +137,8 @@ class BaseConsumer:
                                     .cpu()
                                     .item()
                                 )
-                                filtered_group = self.prompt_level_filtering(group_with_reward)
+                                if self.grpo_config.get("dynamic_batching", True):
+                                    filtered_group = self.prompt_level_filtering(group_with_reward)
                                 recv_effective_count += 1 if filtered_group is not None else 0
                                 self.buffer.append(
                                     [
diff --git a/applications/ColossalChat/coati/distributed/grpo_consumer.py b/applications/ColossalChat/coati/distributed/grpo_consumer.py
index 152689e06..891a6f842 100644
--- a/applications/ColossalChat/coati/distributed/grpo_consumer.py
+++ b/applications/ColossalChat/coati/distributed/grpo_consumer.py
@@ -218,8 +218,6 @@ class GRPOConsumer(BaseConsumer):
 
         if self.grpo_config.get("dynamic_batching", True):
             need_update = self.effective_prompt_count >= self.batch_size * self.dp_size
-            excessive_prompts = self.effective_prompt_count - self.batch_size * self.dp_size
-            assert excessive_prompts <= 0, "Debug: Excessive prompts should always be less than 0. Bug!!!!"
         else:
             # If dynamic batching is disabled, we need to use all samples for training.
             need_update = (step_idx + 1) % self.num_microbatches == 0
@@ -488,7 +486,7 @@ class GRPOConsumer(BaseConsumer):
         else:
             return None
 
-    def calculate_group_reward(self, rollout_group: Dict[str, Any]) -> Dict[str, Any]:
+    def calculate_reward(self, rollout: Dict[str, Any]) -> Dict[str, Any]:
         """
         Calculate the group reward for the given rollout group.
 
@@ -507,20 +505,20 @@ class GRPOConsumer(BaseConsumer):
         Returns:
             Dict[str, Any]: The new group data with calculated reward.
         """
-        reward_group = self.reward_model(
-            rollout_group["input_ids"],
-            gt_answer=rollout_group["gt_answer"],
-            response_idx=rollout_group["response_idx"],
+        reward_model_output = self.reward_model(
+            rollout["input_ids"],
+            gt_answer=rollout["gt_answer"],
+            response_idx=rollout["response_idx"],
         )
         # [num_of_generation]
-        reward = torch.tensor([value[0] for value in reward_group]).to(rollout_group["input_ids"].device)
-        format_acc = torch.tensor([value[1] for value in reward_group]).to(rollout_group["input_ids"].device)
-        ans_acc = torch.tensor([value[2] for value in reward_group]).to(rollout_group["input_ids"].device)
+        reward = torch.tensor([value[0] for value in reward_model_output]).to(rollout["input_ids"].device)
+        format_acc = torch.tensor([value[1] for value in reward_model_output]).to(rollout["input_ids"].device)
+        ans_acc = torch.tensor([value[2] for value in reward_model_output]).to(rollout["input_ids"].device)
 
-        rollout_group["reward"] = reward.view((-1, 1))
-        rollout_group["format_acc"] = format_acc.view((-1, 1))
-        rollout_group["ans_acc"] = ans_acc.view((-1, 1))
-        return rollout_group
+        rollout["reward"] = reward.view((-1, 1))
+        rollout["format_acc"] = format_acc.view((-1, 1))
+        rollout["ans_acc"] = ans_acc.view((-1, 1))
+        return rollout
 
     def prompt_level_filtering(self, rollout_group: Dict[str, Any]) -> Dict[str, Any]:
         """