diff --git a/openvalidators/reward/dpo.py b/openvalidators/reward/dpo.py
index 6cc7d59..21fba3b 100644
--- a/openvalidators/reward/dpo.py
+++ b/openvalidators/reward/dpo.py
@@ -20,7 +20,7 @@
 from typing import List
 from .config import RewardModelType
 from .reward import BaseRewardModel
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, NoRepeatNGramLogitsProcessor
 
 
 class DirectPreferenceRewardModel(BaseRewardModel):
@@ -38,6 +38,7 @@ def __init__(self, device: str):
         self.model = AutoModelForCausalLM.from_pretrained(DirectPreferenceRewardModel.reward_model_name,
                                                           trust_remote_code=True,
                                                           torch_dtype=torch.float16).to(self.device)
+        self.ngram_logit_processor = NoRepeatNGramLogitsProcessor(ngram_size = 5)
 
     def reward_single(self, prompt: str, completion: str, name: str ,with_penalty=True) -> float:
         r""" Calculates a direct preference optimization (DPO) style reward for a completion,
@@ -80,11 +81,10 @@ def reward_single(self, prompt: str, completion: str, name: str ,with_penalty=Tr
             logits = logits[:, :-1, :]  # [batch_size=1, seq_len-1, vocab_len]
 
             if with_penalty:
-                # Apply penalty for repeated generation
-                for i in range(len(prompt_part)+1, len(combined)-1):
-                    logit = logits[:,i,:].clone()
-                    inputs = combined[len(prompt_part):i].clone()
-                    logits[:,i,:] =  self.logit_penalty(input_ids=inputs, logit=logit)
+                org_logit = logits.clone()
+                logits = self.ngram_logit_processor(combined[len(prompt_part):].reshape(1, -1).clone(), logits.permute(0, 2, 1)).permute(0, 2, 1)
+                # ngram_logit_processor set punished tokens to -inf, resetting them to 10 std below instead
+                logits[logits == -float("Inf")] = org_logit.mean() - org_logit.std()*10
 
             # Rescale via log(softmax(logits)).
             logits = logits.log_softmax(-1)
@@ -103,15 +103,4 @@ def get_rewards(self, prompt: str, completions: List[str], name: str) -> torch.F
         rewards = torch.tensor([self.reward_single(prompt, completion, name) for completion in completions],
                                dtype=torch.float32).to(self.device)
         bt.logging.trace(f"DirectPreferenceRewardModel | rewards: {rewards.tolist()}")
-        return rewards
-
-    def logit_penalty(self, input_ids: torch.LongTensor, logit: torch.FloatTensor) -> torch.FloatTensor:
-        # Counts the unique tokens within each generation
-        uniques, counts = input_ids.unique(return_counts=True)
-        score = torch.gather(logit, 1, uniques.unsqueeze(0))
-
-        # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
-        score = torch.where(score < 0, score * (self.penalty**counts), score / (self.penalty**counts))
-
-        logit.scatter_(1, uniques.unsqueeze(0), score.to(logit.dtype))
-        return logit
\ No newline at end of file
+        return rewards
\ No newline at end of file