diff --git a/xtuner/v1/rl/base/controller.py b/xtuner/v1/rl/base/controller.py
index d5f10a15d..8f036ca99 100644
--- a/xtuner/v1/rl/base/controller.py
+++ b/xtuner/v1/rl/base/controller.py
@@ -8,6 +8,7 @@
 
 from xtuner.v1.data_proto.sequence_context import SequenceContext
 from xtuner.v1.model.compose.base import BaseComposeConfig
+from xtuner.v1.ray.utils import free_object_refs
 from xtuner.v1.train.trainer import LoadCheckpointConfig
 from xtuner.v1.utils import ray_method
 
@@ -263,6 +264,13 @@ def fit(self, data_batches: list[ColateItem], pack_max_length: int, rollout_idx:
         try:
             log_infos = ray.get(handles, timeout=TRAIN_RAY_GET_TIMEOUT)
         finally:
+            # free pixel values ref
+            free_pixel_value_refs: list[ray.ObjectRef] = []
+            for data in packed_data_batches:
+                if data["seq_ctx"].pixel_values is not None:
+                    free_pixel_value_refs.extend(data["seq_ctx"].pixel_values)
+            if len(free_pixel_value_refs) > 0:
+                free_object_refs(free_pixel_value_refs)
             del packed_data_batches
         return log_infos
 
diff --git a/xtuner/v1/rl/base/worker.py b/xtuner/v1/rl/base/worker.py
index 5bd6cbcc9..126f6ad1b 100644
--- a/xtuner/v1/rl/base/worker.py
+++ b/xtuner/v1/rl/base/worker.py
@@ -35,7 +35,6 @@
 from xtuner.v1.model.utils.misc import ModelForwardExtraLogInfo
 from xtuner.v1.ray.base import SingleAcceleratorWorker
 from xtuner.v1.ray.config import RolloutConfig
-from xtuner.v1.ray.utils import free_object_refs
 from xtuner.v1.rl.base.loss import BaseRLLossContext
 from xtuner.v1.train.trainer import LoadCheckpointConfig
 from xtuner.v1.utils import (
@@ -485,11 +484,7 @@ def fit(self, data_batches: list[WorkerInputItem], rollout_idx: int) -> WorkerLo
                         f"pixel_values should be list of tensor, got {type(pixel_values)}"
                     )
                     pixel_value_refs = list(pixel_values)
-                    try:
-                        pixel_values = torch.cat(ray.get(pixel_value_refs), dim=0)
-                    finally:
-                        free_object_refs(pixel_value_refs)
-
+                    pixel_values = torch.cat(ray.get(pixel_value_refs), dim=0)
                     seq_ctx.pixel_values = pixel_values
 
             rollout_routed_experts = seq_ctx.rollout_routed_experts
diff --git a/xtuner/v1/train/rl_trainer.py b/xtuner/v1/train/rl_trainer.py
index 62a5c7302..bd7081e19 100644
--- a/xtuner/v1/train/rl_trainer.py
+++ b/xtuner/v1/train/rl_trainer.py
@@ -120,7 +120,7 @@ def get_train_seq_ctx(
 ):
     seq_ctx = SequenceContext.from_input_ids((input_ids,), device="cpu")
     if multimodal_train_info and len(multimodal_train_info) > 0:
-        position_ids = multimodal_train_info.pop("position_ids")  # (1,n) or (3,1,n)
+        position_ids = multimodal_train_info.get("position_ids")  # (1,n) or (3,1,n)
         if position_ids is not None and len(position_ids.shape) == 3:
             # qwen3vl 需要特殊处理，其余的不需要额外处理
             max_value = position_ids.max(dim=-1).values  # (3,1)
@@ -130,9 +130,8 @@ def get_train_seq_ctx(
             position_ids = torch.cat([position_ids, response_position_ids], dim=-1)
             seq_ctx.position_ids = position_ids  # type: ignore[assignment]
             assert position_ids.size(-1) == input_ids.size(-1)
-        seq_ctx.pixel_values = multimodal_train_info.pop("pixel_values")
-        seq_ctx.image_grid_thw = multimodal_train_info.pop("image_grid_thw")
-        del multimodal_train_info
+        seq_ctx.pixel_values = multimodal_train_info.get("pixel_values")
+        seq_ctx.image_grid_thw = multimodal_train_info.get("image_grid_thw")
     return seq_ctx
 
 
@@ -803,6 +802,8 @@ def _prepare_train_data(self, data_groups, pack_max_length, multimodal_train_inf
                     seq_ctx.rollout_routed_experts = routed_experts  # n,layer,expert
 
                 data_batches.append(data_dict)
+            if multimodal_train_info is not None:
+                del multimodal_train_info
         random.shuffle(data_batches)
 
         rewards_t = torch.tensor(rewards_list).float() if rewards_list else torch.tensor([0.0]).float()