diff --git a/src/lmflow/args.py b/src/lmflow/args.py
index 7cb44f86d..9ba9dabce 100644
--- a/src/lmflow/args.py
+++ b/src/lmflow/args.py
@@ -1344,6 +1344,9 @@ class DPOAlignerArguments:
     run_name: Optional[str] = field(
         default="dpo", metadata={"help": "The name of the run."}
     )
+    eval_dataset_path: Optional[str] = field(
+        default=None, metadata={"help": "The path of the eval dataset."}
+    )
 
 
 @dataclass
diff --git a/src/lmflow/pipeline/dpo_aligner.py b/src/lmflow/pipeline/dpo_aligner.py
index 19aed2c2d..42eee808a 100644
--- a/src/lmflow/pipeline/dpo_aligner.py
+++ b/src/lmflow/pipeline/dpo_aligner.py
@@ -71,6 +71,8 @@ def __init__(self, model_args, data_args, aligner_args):
         self.model_args = model_args
         self.data_args = data_args
         self.aligner_args = aligner_args
+        self.train_dataset = None
+        self.eval_dataset = None
 
     def _initialize_trainer(self, model, tokenizer):
         peft_config = LoraConfig(
@@ -118,7 +120,7 @@ def _initialize_trainer(self, model, tokenizer):
             args=training_args,
             beta=self.aligner_args.beta,
             train_dataset=self.train_dataset,
-            eval_dataset=self.eval_dataset,
+            eval_dataset=self.eval_dataset if self.eval_dataset else None,
             tokenizer=tokenizer,
             peft_config=peft_config,
             max_prompt_length=self.aligner_args.beta,
@@ -136,13 +138,14 @@ def _load_dataset(self):
                       and len(x["prompt"]) + len(x["rejected"]) <= self.aligner_args.max_length
         )
         # load evaluation set
-        self.eval_dataset = get_paired_dataset(data_root=self.data_args.dataset_path,
-                                               data_dir="test",
-                                               sanity_check=True)
-        self.eval_dataset = self.eval_dataset.filter(
-            lambda x: len(x["prompt"]) + len(x["chosen"]) <= self.aligner_args.max_length
-                      and len(x["prompt"]) + len(x["rejected"]) <= self.aligner_args.max_length
-        )
+        if self.aligner_args.eval_dataset_path:
+            self.eval_dataset = get_paired_dataset(data_root=self.aligner_args.eval_dataset_path,
+                                                data_dir="test",
+                                                sanity_check=True)
+            self.eval_dataset = self.eval_dataset.filter(
+                lambda x: len(x["prompt"]) + len(x["chosen"]) <= self.aligner_args.max_length
+                        and len(x["prompt"]) + len(x["rejected"]) <= self.aligner_args.max_length
+            )
 
     def align(self, model, dataset, reward_model):
         tokenizer = model.get_tokenizer()