diff --git a/fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py b/fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py
index 69920649a..1b0e3a7cb 100644
--- a/fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py
+++ b/fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py
@@ -24,6 +24,7 @@ from fastdeploy.model_executor.utils import TensorTracker, set_weight_attrs
 from fastdeploy.utils import ceil_div
 
 from ..quantization.quant_base import QuantMethodBase
+from .ep import get_moe_scores
 
 try:
     from fastdeploy.model_executor.ops.gpu import tritonmoe_preprocess_func
@@ -167,13 +168,24 @@ class TritonWeightOnlyMoEMethod(QuantMethodBase):
         moe_intermediate_size = layer.moe_intermediate_size
         hidden_size = layer.hidden_size
 
-        topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
-            gate_out,
-            layer.gate_correction_bias,
-            top_k,
-            True,  # apply_norm_weight,
-            False,
-        )
+        if layer.topk_method == "noaux_tc":
+            _, topk_weights, topk_ids = get_moe_scores(
+                gate_out,
+                layer.n_group,
+                layer.topk_group,
+                layer.top_k,
+                layer.routed_scaling_factor,
+                layer.gate_correction_bias,
+            )
+        else:
+            topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
+                gate_out,
+                layer.gate_correction_bias,
+                layer.top_k,
+                True,  # apply_norm_weight
+                False,
+            )
+
         up_gate_proj_out = paddle.empty(
             [token_num * top_k, moe_intermediate_size * 2],
             dtype=x.dtype,
@@ -419,13 +431,25 @@ class TensorWiseFP8MoEMethod(QuantMethodBase):
         moe_intermediate_size = layer.moe_intermediate_size
         hidden_size = layer.hidden_size
 
-        topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
-            gate_out,
-            layer.gate_correction_bias,
-            top_k,
-            True,  # apply_norm_weight,
-            False,
-        )
+        if layer.topk_method == "noaux_tc":
+
+            _, topk_weights, topk_ids = get_moe_scores(
+                gate_out,
+                layer.n_group,
+                layer.topk_group,
+                layer.top_k,
+                layer.routed_scaling_factor,
+                layer.gate_correction_bias,
+            )
+        else:
+
+            topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
+                gate_out,
+                layer.gate_correction_bias,
+                top_k,
+                True,  # apply_norm_weight,
+                False,
+            )
 
         up_gate_proj_out = paddle.empty(
             [token_num * top_k, moe_intermediate_size * 2],
@@ -829,13 +853,23 @@ class BlockWiseFP8MoEMethod(QuantMethodBase):
         E, N1, _ = getattr(layer, self.added_weight_attrs[0]).shape
         N2 = getattr(layer, self.added_weight_attrs[1]).shape[1]
 
-        topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
-            gate_out,
-            layer.gate_correction_bias,
-            layer.top_k,
-            True,  # apply_norm_weight
-            False,
-        )
+        if layer.topk_method == "noaux_tc":
+            _, topk_weights, topk_ids = get_moe_scores(
+                gate_out,
+                layer.n_group,
+                layer.topk_group,
+                layer.top_k,
+                layer.routed_scaling_factor,
+                layer.gate_correction_bias,
+            )
+        else:
+            topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
+                gate_out,
+                layer.gate_correction_bias,
+                layer.top_k,
+                True,  # apply_norm_weight
+                False,
+            )
 
         config = {
             "BLOCK_SIZE_M": 64,
diff --git a/fastdeploy/rl/rollout_config.py b/fastdeploy/rl/rollout_config.py
index 82074b70c..1fe797868 100644
--- a/fastdeploy/rl/rollout_config.py
+++ b/fastdeploy/rl/rollout_config.py
@@ -60,6 +60,7 @@ class RolloutModelConfig:
         early_stop_config: str = None,
         local_rank: int = 0,
         moba_attention_config: str = None,
+        data_parallel_size: int = 1,
     ):
         # Required parameters
         self.model = model_name_or_path
@@ -95,6 +96,7 @@ class RolloutModelConfig:
         self.splitwise_role = splitwise_role
         self.expert_parallel_size = expert_parallel_size
         self.enable_expert_parallel = enable_expert_parallel
+        self.data_parallel_size = data_parallel_size
         self.ori_vocab_size = ori_vocab_size
         self.quantization = quantization
         self.guided_decoding_backend = guided_decoding_backend