diff --git a/applications/ColossalChat/coati/distributed/reward/agpo_reward.py b/applications/ColossalChat/coati/distributed/reward/agpo_reward.py
new file mode 100644
index 000000000..648de9e3e
--- /dev/null
+++ b/applications/ColossalChat/coati/distributed/reward/agpo_reward.py
@@ -0,0 +1,57 @@
+"""
+Function-based reward verification module.
+"""
+
+from typing import Any, Dict, List
+
+import torch
+
+
+class AGPOReward:
+    def __init__(self, reward_fn: callable, **kwargs: List[Dict[str, Any]]):
+        self.reward_fn = reward_fn
+        self.kwargs = kwargs
+
+    def __call__(
+        self,
+        input_ids: torch.LongTensor,
+        gt_answer: List[torch.Tensor] = None,
+        response_idx: List[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        # Get batch size
+        bs = input_ids.size(0)
+        # Initialize reward
+        rewards = torch.zeros((bs, 3), device=input_ids.device)
+        len_rewards = torch.zeros((bs), device=input_ids.device)
+        num_generations = self.kwargs.get("num_generations")
+
+        # Apply the reward function to the entire batch at once
+        reward_infos = [self.reward_fn(input_ids[i], gt_answer=gt_answer[i], response_idx=response_idx[i], **self.kwargs) for i in range(bs)]
+        reward_batch = torch.stack([info[0] for info in reward_infos])
+        format_acc_batch = torch.stack([info[1] for info in reward_infos])
+        ans_acc_batch = torch.stack([info[2] for info in reward_infos])
+        seq_len_batch = torch.stack([info[3] for info in reward_infos])
+
+        group_reward = reward_batch.view(-1, num_generations)
+        reward_std = group_reward.std(dim=1).repeat_interleave(num_generations, dim=0)
+        mask_zero_std = reward_std == 0
+
+        group_seq_len = seq_len_batch.view(-1, num_generations)
+        group_ans_acc = ans_acc_batch.view(-1, num_generations)
+        mask_incorrect = group_ans_acc == 0
+        masked_seq_len_for_max = group_seq_len.masked_fill(mask_incorrect, float('-inf'))
+        max_group_seq_len = masked_seq_len_for_max.max(dim=1).repeat_interleave(num_generations, dim=0)
+        masked_seq_len_for_min = group_seq_len.masked_fill(mask_incorrect, float('inf'))
+        min_group_seq_len = masked_seq_len_for_min.min(dim=1).repeat_interleave(num_generations, dim=0)
+        len_ratio = (seq_len_batch - min_group_seq_len) / (max_group_seq_len - min_group_seq_len + 1e-6)
+        len_rewards = 0.1 * (1 - len_ratio)
+
+        len_rewards = len_rewards.masked_fill(mask_zero_std, 0.0)
+        len_rewards = len_rewards.masked_fill(mask_incorrect, 0.0)
+        reward_batch += len_rewards
+
+        rewards += torch.stack(
+                [torch.tensor([r, f, a]).to(input_ids.device) for r, f, a in zip(reward_batch, format_acc_batch, ans_acc_batch)], 
+                dim=0
+            )
+        return rewards
diff --git a/applications/ColossalChat/coati/distributed/reward/reward_fn.py b/applications/ColossalChat/coati/distributed/reward/reward_fn.py
index b68c1a92f..198913b49 100644
--- a/applications/ColossalChat/coati/distributed/reward/reward_fn.py
+++ b/applications/ColossalChat/coati/distributed/reward/reward_fn.py
@@ -110,3 +110,28 @@ def boxed_math_reward_fn(input_ids, gt_answer, response_idx, **kwargs):
     reward = reward + length_reward
 
     return torch.tensor([reward, format_acc, ans_acc]).to(input_ids.device)
+
+
+def agpo_boxed_math_reward_fn(input_ids, gt_answer, response_idx, **kwargs):
+    tokenizer = kwargs["tokenizer"]
+    reward_correct = torch.tensor(1.0)
+    reward_wrong = torch.tensor(-1.0)
+    ans_acc = torch.tensor(0.0)
+    format_acc = torch.tensor(0.0)
+    s, e = response_idx[0], response_idx[1]
+    seq_len = torch.tensor(e - s + 1)
+
+    decoded_final_answer = tokenizer.decode(input_ids[s : e + 1], skip_special_tokens=True)
+    gt_answer = tokenizer.decode(gt_answer.squeeze(0), skip_special_tokens=True)
+    final_answer = extract_boxed_solution(decoded_final_answer)
+
+    # Check answer accuracy, answer is considered correct if the answer is correct and the format is valid
+    if final_answer is not None:
+        format_acc += 1
+        if gt_answer.strip().lower() == final_answer.strip().lower():
+            ans_acc += 1
+            reward = reward_correct
+        else:
+            reward = reward_wrong
+
+    return torch.tensor([reward, format_acc, ans_acc, seq_len]).to(input_ids.device)