diff --git a/verl/trainer/ppo/core_algos.py b/verl/trainer/ppo/core_algos.py
index b9790be6..cf4ce7fe 100644
--- a/verl/trainer/ppo/core_algos.py
+++ b/verl/trainer/ppo/core_algos.py
@@ -106,8 +106,9 @@ def compute_gae_advantage_return(token_level_rewards: torch.Tensor, values: torc
         advantages = verl_F.masked_whiten(advantages, eos_mask)
     return advantages, returns
 
+
 # NOTE(sgm): this implementation only consider outcome supervision, where the reward is a scalar.
-def compute_grpo_outcome_advantage(token_level_rewards: torch.Tensor, 
+def compute_grpo_outcome_advantage(token_level_rewards: torch.Tensor,
                                    eos_mask: torch.Tensor,
                                    index: torch.Tensor,
                                    epsilon: float = 1e-6):
@@ -258,14 +259,14 @@ def kl_penalty(logprob: torch.FloatTensor, ref_logprob: torch.FloatTensor, kl_pe
     if kl_penalty == "mse":
         return 0.5 * (logprob - ref_logprob).square()
 
-    # J. Schulman. Approximating kl divergence, 2020. 
+    # J. Schulman. Approximating kl divergence, 2020.
     # # URL http://joschu.net/blog/kl-approx.html.
     if kl_penalty == 'low_var_kl':
         kl = ref_logprob - logprob
         ratio = torch.exp(kl)
         kld = (ratio - kl - 1).contiguous()
         return torch.clamp(kld, min=-10, max=10)
-        
+
     if kl_penalty == "full":
         # so, here logprob and ref_logprob should contain the logits for every token in vocabulary
         raise NotImplementedError
diff --git a/verl/trainer/ppo/ray_trainer.py b/verl/trainer/ppo/ray_trainer.py
index 68ded805..331757a8 100644
--- a/verl/trainer/ppo/ray_trainer.py
+++ b/verl/trainer/ppo/ray_trainer.py
@@ -113,7 +113,6 @@ def apply_kl_penalty(data: DataProto, kl_ctrl: core_algos.AdaptiveKLController,
 
 
 def compute_advantage(data: DataProto, adv_estimator, gamma=1.0, lam=1.0, num_repeat=1):
-    from verl.protocol import fold_batch_dim, unfold_batch_dim
     # prepare response group
     # TODO: add other ways to estimate advantages
     if adv_estimator == 'gae':
@@ -143,7 +142,6 @@ def compute_advantage(data: DataProto, adv_estimator, gamma=1.0, lam=1.0, num_re
                                                                         index=index)
         data.batch['advantages'] = advantages
         data.batch['returns'] = returns
-        data = unfold_batch_dim(data, batch_dims=2)
     else:
         raise NotImplementedError
     return data
@@ -192,7 +190,7 @@ def compute_data_metrics(batch, use_critic=True):
 
     valid_adv = torch.masked_select(advantages, response_mask)
     valid_returns = torch.masked_select(returns, response_mask)
-    
+
     if use_critic:
         values = batch.batch['values']
         valid_values = torch.masked_select(values, response_mask)
@@ -222,19 +220,20 @@ def compute_data_metrics(batch, use_critic=True):
         'critic/advantages/min':
             torch.min(valid_adv).detach().item(),
         # returns
-        'critic/returns/mean': torch.mean(valid_returns).detach().item(),
-        'critic/returns/max': torch.max(valid_returns).detach().item(),
-        'critic/returns/min': torch.min(valid_returns).detach().item(),
-        
+        'critic/returns/mean':
+            torch.mean(valid_returns).detach().item(),
+        'critic/returns/max':
+            torch.max(valid_returns).detach().item(),
+        'critic/returns/min':
+            torch.min(valid_returns).detach().item(),
         **({
-        # values
-        'critic/values/mean': torch.mean(valid_values).detach().item(),
-        'critic/values/max': torch.max(valid_values).detach().item(),
-        'critic/values/min': torch.min(valid_values).detach().item(),
-        # vf explained var
-        'critic/vf_explained_var': (1.0 - return_diff_var / (return_var + 1e-5)).detach().item(),
-        }
-            if use_critic else {}),
+            # values
+            'critic/values/mean': torch.mean(valid_values).detach().item(),
+            'critic/values/max': torch.max(valid_values).detach().item(),
+            'critic/values/min': torch.min(valid_values).detach().item(),
+            # vf explained var
+            'critic/vf_explained_var': (1.0 - return_diff_var / (return_var + 1e-5)).detach().item(),
+        } if use_critic else {}),
 
         # response length
         'response_length/mean':
@@ -628,8 +627,8 @@ def fit(self):
                         # compute rewards. apply_kl_penalty if available
                         if not self.config.actor_rollout_ref.actor.use_kl_loss:
                             batch, kl_metrics = apply_kl_penalty(batch,
-                                                                kl_ctrl=self.kl_ctrl,
-                                                                kl_penalty=self.config.algorithm.kl_penalty)
+                                                                 kl_ctrl=self.kl_ctrl,
+                                                                 kl_penalty=self.config.algorithm.kl_penalty)
                             metrics.update(kl_metrics)
                         else:
                             batch.batch['token_level_rewards'] = batch.batch['token_level_scores']
diff --git a/verl/utils/dataset/rl_dataset.py b/verl/utils/dataset/rl_dataset.py
index 543087ef..48328d7a 100644
--- a/verl/utils/dataset/rl_dataset.py
+++ b/verl/utils/dataset/rl_dataset.py
@@ -141,9 +141,9 @@ def __getitem__(self, item):
         # encode prompts without chat template
         if self.return_raw_chat:
             row_dict['raw_prompt'] = chat.tolist()
-        
+
         # add index for each prompt
         index = row_dict.get("extra_info", {}).get("index", 0)
         row_dict["index"] = index
 
-        return row_dict
\ No newline at end of file
+        return row_dict