diff --git a/swift/llm/template/base.py b/swift/llm/template/base.py
index fce20eb7d2..a402e56af6 100644
--- a/swift/llm/template/base.py
+++ b/swift/llm/template/base.py
@@ -357,11 +357,16 @@ def get_base_model(model):
         else:
             return model
 
-    def _rlhf_encode(self, inputs: TemplateInputs) -> Dict[str, Any]:
+    def _rlhf_encode(self, inputs: TemplateInputs, check_rejected=True) -> Dict[str, Any]:
         chosen = inputs.chosen
         margin = chosen.margin
         chosen_encoded = self._encode_truncated(chosen)
-        rejected_encoded = self._encode_truncated(inputs.rejected)
+        if inputs.rejected is None:
+            if check_rejected:
+                raise ValueError('inputs.rejected is None')
+            rejected_encoded = {}
+        else:
+            rejected_encoded = self._encode_truncated(inputs.rejected)
 
         encoded = {}
         for prefix in ['chosen', 'rejected']:
@@ -373,7 +378,7 @@ def _rlhf_encode(self, inputs: TemplateInputs) -> Dict[str, Any]:
         return encoded
 
     def _kto_encode(self, inputs: TemplateInputs) -> Dict[str, Any]:
-        encoded = self._rlhf_encode(inputs)
+        encoded = self._rlhf_encode(inputs, check_rejected=False)
         encoded['label'] = bool(inputs.chosen.label)
         return encoded
 
@@ -1485,7 +1490,10 @@ def _kto_data_collator(self, batch: List[Dict[str, Any]], *, padding_to: Optiona
         kl_batch = self._fetch_inputs_startswith(batch, 'rejected_')
 
         res = self._data_collator(new_batch, padding_to=padding_to)
-        kl_res = self._data_collator(kl_batch, padding_to=padding_to)
+        if any(kl_batch):
+            kl_res = self._data_collator(kl_batch, padding_to=padding_to)
+        else:
+            kl_res = {}
         res = {
             **{f'completion_{k}': v
                for k, v in res.items()},
diff --git a/swift/llm/train/kto.py b/swift/llm/train/kto.py
index 966c11cb61..63da51013b 100644
--- a/swift/llm/train/kto.py
+++ b/swift/llm/train/kto.py
@@ -41,16 +41,17 @@ def _get_kl_dataset(dataset: Optional[HfDataset],
 
 
 def prepare_kto_dataset(args, train_dataset, val_dataset):
-    world_size = get_dist_setting()[2]
-    if hasattr(args, 'global_batch_size') and args.global_batch_size is not None:
-        total_batch_size = args.global_batch_size
-    else:
-        total_batch_size = (world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps)
-    if total_batch_size <= 1:
-        raise ValueError('Batch size is 1 (too small). KTO will not work properly because the KL term '
-                         'will be equivalent to the implied reward.')
-    train_dataset = _get_kl_dataset(train_dataset, total_batch_size, args.dataset_num_proc, args.data_seed)
-    val_dataset = _get_kl_dataset(val_dataset, total_batch_size, args.dataset_num_proc, args.data_seed)
+    if args.loss_type != 'apo_zero_unpaired':
+        world_size = get_dist_setting()[2]
+        if hasattr(args, 'global_batch_size') and args.global_batch_size is not None:
+            total_batch_size = args.global_batch_size
+        else:
+            total_batch_size = (world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps)
+        if total_batch_size <= 1:
+            raise ValueError('Batch size is 1 (too small). KTO will not work properly because the KL term '
+                             'will be equivalent to the implied reward.')
+        train_dataset = _get_kl_dataset(train_dataset, total_batch_size, args.dataset_num_proc, args.data_seed)
+        val_dataset = _get_kl_dataset(val_dataset, total_batch_size, args.dataset_num_proc, args.data_seed)
 
     label = train_dataset['label']
     num_desirable = max(sum(label), 1)
diff --git a/swift/megatron/trainers/kto_trainer.py b/swift/megatron/trainers/kto_trainer.py
index f201767d3e..9ddb8ab343 100644
--- a/swift/megatron/trainers/kto_trainer.py
+++ b/swift/megatron/trainers/kto_trainer.py
@@ -76,7 +76,7 @@ def loss_func(self, output_tensor, *, data, kl_data, label):
         loss = loss.mean()
         mean_metric = {
             'loss': loss.detach().clone(),
-            'kl': kl.detach(),
+            'kl': kl.squeeze().detach(),
         }
         metric = self._all_reduce_metric(mean_metric)
         sum_metric = {
@@ -159,7 +159,11 @@ def _prepare_batch(self, data, vp_stage):
         num_samples = data.pop('num_samples')
         for key in ['completion_', 'KL_completion_']:
             _data = {k[len(key):]: v for k, v in data.items() if k.startswith(key)}
-            res.append(super()._prepare_batch(_data, vp_stage, num_samples))
+            if not self.args.calculate_KL and key == 'KL_completion_':
+                _data = {}
+            else:
+                _data = super()._prepare_batch(_data, vp_stage, num_samples)
+            res.append(_data)
         res[0]['label'] = data['label']
         return res