diff --git a/nemo_aligner/models/nlp/gpt/megatron_gpt_dpo_model.py b/nemo_aligner/models/nlp/gpt/megatron_gpt_dpo_model.py
index 71d486818..ece8e34da 100644
--- a/nemo_aligner/models/nlp/gpt/megatron_gpt_dpo_model.py
+++ b/nemo_aligner/models/nlp/gpt/megatron_gpt_dpo_model.py
@@ -46,6 +46,7 @@
     "kto": "kto_loss_func",
 }
 
+
 class MegatronGPTDPOModel(MegatronGPTModel, SupervisedInterface):
     """
     Megatron GPT DPO Model Training.
@@ -204,14 +205,16 @@ def get_reduced_masked_logps(self, logps, labels, average_log_probs=False):
             return (logps * loss_mask).sum(-1)
 
     def loss_func(self, pi_logprobs, ref_logprobs, labels, average_log_probs=False):
-        loss, chosen_rewards, reject_rewards = self.preference_loss(pi_logprobs, ref_logprobs, labels, average_log_probs)
+        loss, chosen_rewards, reject_rewards = self.preference_loss(
+            pi_logprobs, ref_logprobs, labels, average_log_probs
+        )
 
         with torch.no_grad():
             comp = chosen_rewards > reject_rewards
             acc_chosen = comp.float().mean()
 
         return loss, acc_chosen
-    
+
     def dpo_loss_func(self, pi_logprobs, ref_logprobs, labels, average_log_probs=False):
         rewards = self.get_reduced_masked_logps(
             pi_logprobs - ref_logprobs, labels, average_log_probs=average_log_probs
@@ -245,9 +248,7 @@ def kto_loss_func(self, pi_logprobs, ref_logprobs, labels, average_log_probs=Fal
 
         chosen_rewards, reject_rewards = self.split_output_tensor(rewards)
 
-        rewards_kl = self.get_reduced_masked_logps(
-             pi_logprobs - ref_logprobs, labels, average_log_probs=True
-        )            
+        rewards_kl = self.get_reduced_masked_logps(pi_logprobs - ref_logprobs, labels, average_log_probs=True)
 
         chosen_kl, reject_kl = self.split_output_tensor(rewards_kl)
         loss = (