diff --git a/unsloth/models/rl_replacements.py b/unsloth/models/rl_replacements.py
index 41b22d486f..83deea5261 100644
--- a/unsloth/models/rl_replacements.py
+++ b/unsloth/models/rl_replacements.py
@@ -208,8 +208,8 @@ def grpo_trainer__get_per_token_logps(function_name, function):
     if  function_name != "_get_per_token_logps": return function
 
     def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep):
-        if os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0':
-            return None # Unsloth efficient GRPO
+        # if os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0':
+        #     return None # Unsloth efficient GRPO
         # Otherwise, calculate normally:
         if not hasattr(self, '_autocast_dtype'):
             self._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16
@@ -260,8 +260,13 @@ def grpo_trainer_compute_loss(function_name, function):
         logits_to_keep = completion_ids.size(1)  # we only need to compute the logits for the completion tokens
         _input_ids = input_ids
         _logits_to_keep = logits_to_keep
-        per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
 
+        if os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '1':
+            attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
+            per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
+        else:
+            per_token_logps = None
+        
         # Compute the KL divergence between the model and the reference model
         ref_per_token_logps = inputs["ref_per_token_logps"]
         # per_token_kl = torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1