diff --git a/applications/Chat/coati/models/loss.py b/applications/Chat/coati/models/loss.py
index 7fc437d90..926c6e2a4 100644
--- a/applications/Chat/coati/models/loss.py
+++ b/applications/Chat/coati/models/loss.py
@@ -65,7 +65,7 @@ class ValueLoss(nn.Module):
         surr2 = (values - reward)**2
         loss = torch.max(surr1, surr2)
         loss = loss.mean()
-        return loss
+        return 0.5 * loss
 
 
 class PPOPtxActorLoss(nn.Module):
diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py
index 2b0cfcc16..d58e437e6 100644
--- a/applications/Chat/coati/trainer/ppo.py
+++ b/applications/Chat/coati/trainer/ppo.py
@@ -32,6 +32,7 @@ class PPOTrainer(Trainer):
         buffer_limit (int, defaults to 0): the max_size limitaiton of replay buffer
         buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu
         eps_clip (float, defaults to 0.2): the clip coefficient of policy loss
+        vf_coef (float, defaults to 1.0): the coefficient of value loss
         value_clip (float, defaults to 0.4): the clip coefficient of value loss
         experience_batch_size (int, defaults to 8): the batch size to use for experience generation
         max_epochs (int, defaults to 1): the number of epochs of training process
@@ -56,6 +57,7 @@ class PPOTrainer(Trainer):
                  buffer_limit: int = 0,
                  buffer_cpu_offload: bool = True,
                  eps_clip: float = 0.2,
+                 vf_coef: float = 1.0,
                  value_clip: float = 0.4,
                  experience_batch_size: int = 8,
                  max_epochs: int = 1,
@@ -74,6 +76,7 @@ class PPOTrainer(Trainer):
 
         self.actor_loss_fn = PolicyLoss(eps_clip)
         self.critic_loss_fn = ValueLoss(value_clip)
+        self.vf_coef = vf_coef
         self.ptx_loss_fn = nn.CrossEntropyLoss(ignore_index=-100)
         self.ptx_coef = ptx_coef
         self.actor_optim = actor_optim
@@ -112,6 +115,7 @@ class PPOTrainer(Trainer):
                                           experience.values,
                                           experience.reward,
                                           action_mask=experience.action_mask)
+        critic_loss = critic_loss * self.vf_coef
         self.strategy.backward(critic_loss, self.critic, self.critic_optim)
         self.strategy.optimizer_step(self.critic_optim)
         self.critic_optim.zero_grad()