diff --git a/unsloth/models/rl_replacements.py b/unsloth/models/rl_replacements.py
index ee57055a00..b1a2ba8f70 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):
@@ -221,7 +222,7 @@ 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)
+        # per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
 
         # Compute the KL divergence between the model and the reference model
         ref_per_token_logps = inputs["ref_per_token_logps"]
@@ -233,25 +234,13 @@ 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:]
-        loss, completion_length, mean_kl = grpo_compute_loss(
-            ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages, bsz,
-        )
+        # loss, completion_length, mean_kl = grpo_compute_loss(
+        #     ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages, bsz,
+        # )
         accumulated_loss, accumulated_completion_length, accumulated_mean_kl = grpo_accumulated_loss(
-            self, _input_ids, logits_to_keep, completion_mask, advantages, n_chunks = 1,
+            self, _input_ids, logits_to_keep, completion_mask, advantages, n_chunks = 2,
         )
-        print("loss", loss, accumulated_loss)
-        print("completion_length", completion_length, accumulated_completion_length)
-        print("mean_kl", mean_kl, accumulated_mean_kl)
-
-        from unsloth_zoo.rl_replacements import RL_REPLACEMENTS
-        RL_REPLACEMENTS["data"] = (
-            ref_per_token_logps.detach(), per_token_logps.detach(), _input_ids, completion_mask, self.beta, advantages,
-            loss.detach(), completion_length, mean_kl, completion_ids, _logits_to_keep,
-        )
-        if "count" in RL_REPLACEMENTS:
-            RL_REPLACEMENTS["count"] += 1
-            if RL_REPLACEMENTS["count"] == 10: raise
-        else: RL_REPLACEMENTS["count"] = 1
+        loss, completion_length, mean_kl = accumulated_loss, accumulated_completion_length, accumulated_mean_kl
         # 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())