diff --git a/trinity/service/data_juicer/server/session.py b/trinity/service/data_juicer/server/session.py
index 5d70e0e5f1..c641f573d8 100644
--- a/trinity/service/data_juicer/server/session.py
+++ b/trinity/service/data_juicer/server/session.py
@@ -11,6 +11,7 @@
     group_scores,
     parse_config,
 )
+from trinity.utils.log import get_logger
 
 
 def extract_metrics(dataset: Dataset) -> Dict:
@@ -39,6 +40,12 @@ def __init__(self, config: DJConfig):
             "usage_frequency": -0.5,
             "quality": 1.0,
         }
+        self.order_method = self.config.order_method
+        self.order_args = self.config.order_args or {
+            "folding_layers": 3,
+        }
+
+        self.logger = get_logger(__name__)
 
     def process_experience(self, ds: Dataset) -> Tuple[Dataset, Dict]:
         """Process a batch of experiences.
@@ -74,11 +81,45 @@ def process_task(self) -> Dict:
         )
         ds = ds.map(compute_priority_scores_func)
         # sort the output dataset in priority
-        if "priority" in ds.features:
-            top_k = self.config.top_k
-            if top_k == -1:
-                top_k = ds.num_rows
-            ds = ds.sort("priority", reverse=True).take(top_k)
+        ds = self.order_task(ds)
         # export to the target directory
         ds.to_json(os.path.join(self.config.output_dir, "output.jsonl"))  # type: ignore [arg-type]
         return {"sample_num": ds.num_rows}
+
+    def order_task(self, dataset: Dataset) -> Dataset:
+        """
+        Order the dataset with specified method.
+        """
+        # check if priority field exists
+        if "priority" not in dataset.features and self.order_method in {"sort", "folding"}:
+            self.logger.warning(
+                f'"priority" field not found for {self.order_method}. Use "keep" instead.'
+            )
+            self.order_method = "keep"
+
+        # get top-k
+        top_k = self.config.top_k
+        if top_k == -1:
+            top_k = dataset.num_rows
+
+        if self.order_method == "keep":
+            # keep the original order
+            return dataset
+        elif self.order_method == "shuffle":
+            # shuffle the dataset
+            return dataset.shuffle()
+        elif self.order_method == "sort":
+            # sort the dataset acording to priority
+            return dataset.sort("priority", reverse=True).take(top_k)
+        elif self.order_method == "folding":
+            # folding the dataset to repeat the curriculum learning
+            # Reference: https://arxiv.org/abs/2506.21545
+            sorted_dataset = dataset.sort("priority", reverse=True).take(top_k)
+            folding_layers = self.order_args.get("folding_layers", 3)
+            folding_indices = []
+            for j in range(folding_layers):
+                partition = list(range(j, dataset.num_rows, folding_layers))
+                folding_indices.extend(partition)
+            return sorted_dataset.select(folding_indices)
+        else:
+            raise ValueError(f"Invalid order method: {self.order_method}")
diff --git a/trinity/service/data_juicer/server/utils.py b/trinity/service/data_juicer/server/utils.py
index 7ea7961bf4..0a8d6cc784 100644
--- a/trinity/service/data_juicer/server/utils.py
+++ b/trinity/service/data_juicer/server/utils.py
@@ -24,6 +24,8 @@ class DJConfig(BaseModel):
     target_fields: List[str] = []  # fields in the output dataset
     priority_weights: Dict[str, float] = {}  # weights for priority computing
     top_k: int = -1  # number of samples to select after task pipeline. -1 means all
+    order_method: Literal["keep", "shuffle", "sort", "folding"] = "sort"
+    order_args: Dict = {}
 
     @model_validator(mode="after")
     def check_dj_config(self):