diff --git a/nemo_aligner/algorithms/self_rewarding.py b/nemo_aligner/algorithms/self_rewarding.py
index 5f50b4c4f..71ff253c7 100644
--- a/nemo_aligner/algorithms/self_rewarding.py
+++ b/nemo_aligner/algorithms/self_rewarding.py
@@ -86,7 +86,7 @@ def self_rewarding_custom_collate(batch, eos_id):
         "answers_only": answer_ids,
         "prompt_lengths": context_lengths,
         "combined_lengths": combined_lengths,
-        "dataset_mask": batch[0]['metadata']['mask'] if 'metadata' in batch[0] else None,
+        "dataset_mask": batch[0]['metadata']['mask'] if 'metadata' in batch[0] else "",
     }
 
     return output
@@ -588,6 +588,8 @@ def get_rewards_meta(self, list_of_batches):
         reward_scores = [[] for _ in range(sum([len(b["prompt_lengths"]) for b in list_of_batches]))]
         reward_scores = []
         reward_responses, prompt_lengths, resp_lengths, is_end = self.get_generations(list_of_batches)
+        if torch.distributed.get_rank() == 0 and torch.distributed.get_rank() == parallel_state.get_data_parallel_src_rank():
+            print(f"*** META_PROMPT_AND_RESP  [ {self.tokenizer.ids_to_text(reward_responses[0].tolist())} ]")
         batch_responses_str = []
         for t, s, e in zip(reward_responses, prompt_lengths.tolist(), resp_lengths.tolist()):
             response = self.tokenizer.ids_to_text(t[s:e].tolist())
@@ -1056,6 +1058,7 @@ def augment_dataloader(self, dataloader):
                             orig_response_str = self.tokenizer.ids_to_text(
                                 cand_for_meta[1][cand_for_meta[2] : cand_for_meta[3]].tolist()
                             )
+                            norm_prompt_str, norm_response_str = self.normalise_prompt(orig_prompt_str, orig_response_str, buffer[0]["dataset_mask"])
                             meta_batch = []
                             for a, b in itertools.combinations(
                                 [self.tokenizer.ids_to_text(s[0][s[1] : s[2]].tolist()) for s in reward_tokens_raw], 2
@@ -1069,10 +1072,10 @@ def augment_dataloader(self, dataloader):
                                 a = re.sub("(?i)(?:Score|Points): ([0-9\.]+)", "", a)
                                 b = re.sub("(?i)(?:Score|Points): ([0-9\.]+)", "", b)
                                 meta_str_ab = self.meta_judge_template_fn(
-                                    prompt=orig_prompt_str, response=orig_response_str, judgement_a=a, judgement_b=b
+                                    prompt=norm_prompt_str, response=norm_response_str, judgement_a=a, judgement_b=b
                                 )
                                 meta_str_ba = self.meta_judge_template_fn(
-                                    prompt=orig_prompt_str, response=orig_response_str, judgement_a=b, judgement_b=a
+                                    prompt=norm_prompt_str, response=norm_response_str, judgement_a=b, judgement_b=a
                                 )
                                 meta_tokens_ab = self.model.tokenizer.text_to_ids(meta_str_ab)
                                 meta_tokens_ba = self.model.tokenizer.text_to_ids(meta_str_ba)