diff --git a/unsloth/models/rl_replacements.py b/unsloth/models/rl_replacements.py
index e343a2ec70..6ea8547adc 100644
--- a/unsloth/models/rl_replacements.py
+++ b/unsloth/models/rl_replacements.py
@@ -320,16 +320,11 @@ def grpo_trainer_compute_loss(function_name, function):
         # _prepare_inputs doesn't return reference log probs anymore. We need to calculate it ourselves.
         # https://github.com/huggingface/trl/blob/05bc43e960396581e458195b8388efe6b82cae1f/trl/trainer/grpo_trainer.py#L1328
         if self.beta != 0.0:
-            print("!!!!!!!!!!")
-            print("!!!!!!!!!!")
             with torch.inference_mode(), model.disable_adapter():
                 ref_per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
         else:
             ref_per_token_logps = None
         # per_token_kl = torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1
-        print("!!!!!!!!!!")
-        print("!!!!!!!!!!")
-        print(ref_per_token_logps)
         # x - x.detach() allows for preserving gradients from x
         advantages = inputs["advantages"]
         # per_token_loss = torch.exp(per_token_logps - per_token_logps.detach()) * advantages.unsqueeze(1)
@@ -339,10 +334,13 @@ def grpo_trainer_compute_loss(function_name, function):
             old_hidden_states = inputs["old_per_token_logps"]
         else:
             old_hidden_states = None
+
         input_ids = input_ids[:, -logits_to_keep:]
         if per_token_logps is not None:
 
-            ref_per_token_logps = ref_per_token_logps[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
+            if ref_per_token_logps is not None:
+                ref_per_token_logps = ref_per_token_logps[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
+
             per_token_logps = per_token_logps[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
             
             loss, completion_length, mean_kl = grpo_compute_loss_slow(