diff --git a/examples/v1/config/rl_qwen25_7B_dapo.py b/examples/v1/config/rl_dapo_math.py
similarity index 99%
rename from examples/v1/config/rl_qwen25_7B_dapo.py
rename to examples/v1/config/rl_dapo_math.py
index 96e94795c..500ec31bd 100644
--- a/examples/v1/config/rl_qwen25_7B_dapo.py
+++ b/examples/v1/config/rl_dapo_math.py
@@ -57,7 +57,7 @@
     gpu_memory_utilization=0.8,
     context_length=max_response_length + max_prompt_length,
     enable_return_routed_experts=(enable_return_routed_experts == "1"),
-    rollout_max_batch_size_per_instance=1024
+    rollout_max_batch_size_per_instance=2048
 )
 
 # 3. judger
diff --git a/examples/v1/config/rl_qwen25_7B_dapo_async.py b/examples/v1/config/rl_dapo_math_async.py
similarity index 99%
rename from examples/v1/config/rl_qwen25_7B_dapo_async.py
rename to examples/v1/config/rl_dapo_math_async.py
index 0958dfec8..3beeea21e 100644
--- a/examples/v1/config/rl_qwen25_7B_dapo_async.py
+++ b/examples/v1/config/rl_dapo_math_async.py
@@ -57,7 +57,7 @@
     gpu_memory_utilization=0.8,
     context_length=max_response_length + max_prompt_length,
     enable_return_routed_experts=(enable_return_routed_experts == "1"),
-    rollout_max_batch_size_per_instance=1024
+    rollout_max_batch_size_per_instance=2048
 )
 
 # 3. judger
diff --git a/examples/v1/config/rl_qwen25_7B_dapo_aysnc_filter.py b/examples/v1/config/rl_dapo_math_aysnc_filter.py
similarity index 100%
rename from examples/v1/config/rl_qwen25_7B_dapo_aysnc_filter.py
rename to examples/v1/config/rl_dapo_math_aysnc_filter.py
diff --git a/xtuner/v1/data_proto/rl_data.py b/xtuner/v1/data_proto/rl_data.py
index e8009eb34..4aa93d91c 100644
--- a/xtuner/v1/data_proto/rl_data.py
+++ b/xtuner/v1/data_proto/rl_data.py
@@ -84,11 +84,18 @@ class RolloutState(CacheObj, BaseModel):
     sample_params: SampleParams = SampleParams()
 
     # --- InferEngine 输出 ---
+    # 每一次推理引擎的实际输出, 在rollout worker中被覆盖写
     response: str | None = None
     response_ids: list[int] | None = None
     logprobs: list[float] | None = None
     routed_experts: list[int] | RayObjectRef | None = None
     finish_reason: str | None = None
+    # response_mask: 记录response_ids中哪个token算loss, 与response_ids长度相同，每轮rollout在 agent_loop.generate 中覆盖写
+    response_mask: list[int] | None = None
+    # response_rollout_steps：记录 response_ids 中每个 token 是在哪个 rollout_step 生成的，与 response_ids 长度相同，每轮rollout在agent_loop中后处理中覆盖写
+    response_rollout_steps: list[int] | None = None
+    # 记录该样本过期程度，即最先生成的token与当前的训练步数的差值，数值越大表示越过期，在 agent_loop 中后处理中覆盖写
+    seq_staleness: int = 0
 
     #  --- Judger 输出 ---
     reward: dict[str, Any] | None = None
@@ -98,13 +105,10 @@ class RolloutState(CacheObj, BaseModel):
     task_name: str | None = None
     status: Status = Status.INIT
     error_msg: str | None = None
-    seq_staleness: int = 0
-    response_mask: list[int] | None = None  # response_ids的长度
-    response_rollout_steps: list[int] | None = None  # 记录 response_ids 中每个 token 是在哪个 rollout_step 生成的
     extra_fields: dict[str, Any] = {}
 
     @field_serializer("routed_experts")
-    def _serialize_routed_experts(self, value: list[int] | RayObjectRef | None) -> list[int] | None:
+    def _serialize_routed_experts(self, value: torch.Tensor | RayObjectRef | None) -> list | None:
         """Dump 时跳过 ray.ObjectRef，序列化为 None，避免 PydanticSerializationError。"""
         if value is None:
             return None
@@ -117,7 +121,7 @@ def _serialize_routed_experts(self, value: list[int] | RayObjectRef | None) -> l
             pass
         if type(value).__name__ == "ObjectRef" and "ray" in getattr(type(value), "__module__", ""):
             return None
-        return value  # list[int]
+        return value.tolist()
 
 
 def update_status_from_finish_reason(finish_reason: str | None) -> Status:
diff --git a/xtuner/v1/rl/agent_loop/utils.py b/xtuner/v1/rl/agent_loop/utils.py
index b6054e043..4606bd889 100644
--- a/xtuner/v1/rl/agent_loop/utils.py
+++ b/xtuner/v1/rl/agent_loop/utils.py
@@ -1,3 +1,7 @@
+import time
+
+import ray
+
 from xtuner.v1.data_proto import RolloutState, Status, update_seq_staleness
 from xtuner.v1.utils import get_logger
 
@@ -5,6 +9,17 @@
 logger = get_logger()
 
 
+def _resolve_routed_experts(routed_experts: list[int] | ray.ObjectRef | None) -> list[int] | None:
+    if routed_experts is None:
+        return None
+    if isinstance(routed_experts, ray.ObjectRef):
+        routed_experts = ray.get(routed_experts)
+    if hasattr(routed_experts, "tolist"):
+        routed_experts = routed_experts.tolist()
+    assert isinstance(routed_experts, list), f"Unexpected routed_experts type: {type(routed_experts)}"
+    return routed_experts
+
+
 class PartialRolloutHandler:
     """Handle preprocessing and postprocessing for partial rollout
     continuation."""
@@ -47,6 +62,7 @@ def preprocess(self, rollout_state: RolloutState, enable_partial_rollout: bool =
             "response": rollout_state.response or "",
             "logprobs": rollout_state.logprobs or [],
             "response_mask": rollout_state.response_mask or [],
+            "routed_experts": rollout_state.routed_experts,
         }
         return rollout_state
 
@@ -63,5 +79,40 @@ def postprocess(self, rollout_state: RolloutState, rollout_step: int) -> Rollout
         rollout_state.response = history_dict.get("response", "") + (rollout_state.response or "")
         rollout_state.logprobs = history_dict.get("logprobs", []) + (rollout_state.logprobs or [])
         rollout_state.response_mask = history_dict.get("response_mask", []) + (rollout_state.response_mask or [])
+        history_routed_experts_ref = history_dict.get("routed_experts")
+        cur_routed_experts_ref = rollout_state.routed_experts
+        if history_routed_experts_ref is not None and cur_routed_experts_ref is not None:
+            start_time = time.time()
+            history_routed_experts = _resolve_routed_experts(history_dict.get("routed_experts"))
+            cur_routed_experts = _resolve_routed_experts(rollout_state.routed_experts)
+            cur_routed_experts_len = len(cur_routed_experts)
+            history_routed_experts_len = len(history_routed_experts)
+            assert history_routed_experts_len - 1 <= cur_routed_experts_len, (
+                f"Existing routed_experts len: {history_routed_experts_len}, current routed_experts len: {cur_routed_experts_len}"
+            )
+            cur_routed_experts = cur_routed_experts[history_routed_experts_len:]
+            concat_routed_experts = history_routed_experts + cur_routed_experts
+
+            prompt_ids = rollout_state.prompt_ids or []
+            response_ids = rollout_state.response_ids or []
+            expect_tokens_num = len(prompt_ids) + len(response_ids) - 1
+            assert len(concat_routed_experts) == expect_tokens_num, (
+                f"After concatenation, routed_experts len: {len(concat_routed_experts)}, expected tokens num: {expect_tokens_num}"
+            )
+            logger.info(
+                f"[PartialRolloutHandler] Postprocess rollout {rollout_state.uid}: "
+                f"concat routed_experts len={len(concat_routed_experts)} "
+                f"(history={history_routed_experts_len}, new={cur_routed_experts_len}), "
+                f"prompt={len(prompt_ids)}, response={len(response_ids)}"
+            )
+            rollout_state.routed_experts = ray.put(concat_routed_experts)
+            end_time = time.time()
+            logger.info(
+                f"[PartialRolloutHandler] Postprocess routed_experts concatenation time: {end_time - start_time:.4f} seconds"
+            )
+        elif history_routed_experts_ref is None and cur_routed_experts_ref is not None:
+            rollout_state.routed_experts = cur_routed_experts_ref
+        elif history_routed_experts_ref is not None and cur_routed_experts_ref is None:
+            rollout_state.routed_experts = history_routed_experts_ref
 
         return rollout_state