diff --git a/unsloth/models/rl_replacements.py b/unsloth/models/rl_replacements.py
index ad6d7822ac..f2ac7f80de 100644
--- a/unsloth/models/rl_replacements.py
+++ b/unsloth/models/rl_replacements.py
@@ -177,6 +177,7 @@ 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):
+        return None
         if not hasattr(self, '_autocast_dtype'):
             self._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16
         with torch.amp.autocast(device_type = 'cuda', dtype = self._autocast_dtype):
@@ -201,6 +202,8 @@ RL_FUNCTIONS["grpo_trainer"].append(grpo_trainer__get_per_token_logps)
 grpo_compute_loss = RL_REPLACEMENTS["grpo_compute_loss"]
 RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(grpo_compute_loss))
 
+global INPUTS
+
 # Edit _get_per_token_logps to handle mixed precision
 def grpo_trainer_compute_loss(function_name, function):
     if  function_name != "compute_loss": return function
@@ -229,10 +232,15 @@ def grpo_trainer_compute_loss(function_name, function):
         # per_token_loss = -(per_token_loss - self.beta * per_token_kl)
         # loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
         input_ids = input_ids[:, -logits_to_keep:]
-        print(input_ids.shape, ref_per_token_logps.shape, per_token_logps.shape, completion_mask.shape, advantages.shape)
         loss, completion_length, mean_kl = grpo_compute_loss(
             ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages,
         )
+        global INPUTS
+        INPUTS = (
+            ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages,
+            loss, completion_length, mean_kl,
+        )
+        raise
         # Log the metrics
         # completion_length = self.accelerator.gather_for_metrics(completion_mask.sum(1)).float().mean().item()
         self._metrics["completion_length"].append(completion_length.item())