diff --git a/recipe/fully_async_policy/shell/dapo_30b_a3b_base_math_fsdp.sh b/recipe/fully_async_policy/shell/dapo_30b_a3b_base_math_fsdp.sh
new file mode 100644
index 00000000000..c061ad2b1ee
--- /dev/null
+++ b/recipe/fully_async_policy/shell/dapo_30b_a3b_base_math_fsdp.sh
@@ -0,0 +1,191 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+project_name='DAPO-Qwen3-30B-A3B-Base-Async'
+exp_name='Fsdp2-tp4sp4'
+
+# Ray
+RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"}
+WORKING_DIR=${WORKING_DIR:-"${PWD}"}
+RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"}
+# Paths
+DATA_PATH=${RAY_DATA_HOME:-"${HOME}/verl"}
+DATA_PATH=${DATA_PATH:-"/mnt/bn/${BYTENAS}"}
+# very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface
+MODEL_PATH=${MODEL_PATH:-"${DATA_PATH}/shared/models/Qwen3-30B-A3B-Base"}
+CKPTS_DIR=${CKPTS_DIR:-"${DATA_PATH}/ckpts/${project_name}/${exp_name}"}
+TRAIN_FILE=${TRAIN_FILE:-"${DATA_PATH}/shared/data/dapo-math/dapo-math-17k.parquet"}
+TEST_FILE=${TEST_FILE:-"${DATA_PATH}/shared/data/dapo-math/aime-2024.parquet"}
+
+
+rollout_mode="async"
+rollout_name="vllm" # sglang or vllm
+if [ "$rollout_mode" = "async" ]; then
+    export VLLM_USE_V1=1
+    return_raw_chat="True"
+fi
+
+# Algorithm parameters
+adv_estimator=grpo
+
+use_kl_in_reward=False
+kl_coef=0.0
+use_kl_loss=False
+kl_loss_coef=0.0
+
+clip_ratio_low=0.2
+clip_ratio_high=0.28
+
+# Response length parameters
+max_prompt_length=$((1024 * 2))
+max_response_length=$((1024 * 20))
+enable_overlong_buffer=True
+overlong_buffer_len=$((1024 * 4))
+overlong_penalty_factor=1.0
+
+# Training parameters
+loss_agg_mode="token-mean"
+enable_filter_groups=True
+filter_groups_metric=acc
+max_num_gen_batches=10
+
+# Algorithm
+temperature=1.0
+top_p=1.0
+top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
+val_top_p=0.7
+
+
+NNODES=${NNODES:-4}
+NGPUS_PER_NODE=${NGPUS_PER_NODE:-8}
+
+# Fully async specific parameters
+n_gpus_rollout=8
+n_gpus_training=8 
+n_nodes_rollout=2 
+n_nodes_train=2 # $((NNODES - n_nodes_rollout))
+
+train_bsz=512
+train_prompt_bsz=0
+gen_prompt_bsz=1
+n_resp_per_prompt=16
+train_prompt_mini_bsz=32
+total_rollout_steps=$(((train_bsz * 400)))
+test_freq=25
+staleness_threshold=0.6 # 0 0.3 1
+require_batches=1
+total_train_gpus=$((n_gpus_training * n_nodes_train))
+total_rollout_gpus=$((n_gpus_rollout * n_nodes_rollout))
+trigger_parameter_sync_step=$((train_bsz / ( train_prompt_mini_bsz * require_batches))) # 8 16 32
+partial_rollout=True
+enforce_eager=False
+nccl_timeout=72000
+enable_sleep_mode=False
+
+# Performance Related Parameter
+sp_size=4
+use_dynamic_bsz=True
+actor_ppo_max_token_len=$((max_prompt_length + max_response_length))
+infer_ppo_max_token_len=$((max_prompt_length + max_response_length))
+ref_offload=True
+actor_offload=False
+gen_tp=4
+fsdp_size=-1
+
+
+ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \
+    --working-dir "${WORKING_DIR}" \
+    --address "${RAY_ADDRESS}" \
+    -- python3 -m recipe.fully_async_policy.fully_async_main \
+    --config-path=config \
+    --config-name='fully_async_dapo_trainer.yaml' \
+    data.train_files="${TRAIN_FILE}" \
+    data.val_files="${TEST_FILE}" \
+    data.prompt_key=prompt \
+    data.truncation='left' \
+    actor_rollout_ref.actor.strategy=fsdp \
+    critic.strategy=fsdp \
+    data.max_prompt_length=${max_prompt_length} \
+    data.max_response_length=${max_response_length} \
+    data.train_batch_size=${train_prompt_bsz} \
+    data.gen_batch_size=${gen_prompt_bsz} \
+    data.return_raw_chat=${return_raw_chat} \
+    actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
+    algorithm.adv_estimator=${adv_estimator} \
+    algorithm.use_kl_in_reward=${use_kl_in_reward} \
+    algorithm.kl_ctrl.kl_coef=${kl_coef} \
+    actor_rollout_ref.rollout.calculate_log_probs=True \
+    actor_rollout_ref.nccl_timeout=${nccl_timeout} \
+    actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \
+    actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \
+    actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \
+    actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \
+    actor_rollout_ref.actor.clip_ratio_c=10.0 \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.hybrid_engine=False \
+    +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \
+    actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
+    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.model.path="${MODEL_PATH}" \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
+    actor_rollout_ref.actor.optim.weight_decay=0.1 \
+    actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
+    actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.actor.grad_clip=1.0 \
+    actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
+    actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.50 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \
+    actor_rollout_ref.rollout.enable_chunked_prefill=True \
+    +actor_rollout_ref.rollout.enable_sleep_mode=${enable_sleep_mode} \
+    actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
+    actor_rollout_ref.rollout.enforce_eager=${enforce_eager} \
+    actor_rollout_ref.rollout.temperature=${temperature} \
+    actor_rollout_ref.rollout.top_p=${top_p} \
+    actor_rollout_ref.rollout.top_k=${top_k} \
+    actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \
+    actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \
+    actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \
+    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
+    actor_rollout_ref.rollout.val_kwargs.n=1 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \
+    actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \
+    actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \
+    actor_rollout_ref.rollout.name=${rollout_name} \
+    actor_rollout_ref.rollout.mode=${rollout_mode} \
+    reward_model.reward_manager=dapo \
+    reward_model.overlong_buffer.enable=${enable_overlong_buffer} \
+    reward_model.overlong_buffer.len=${overlong_buffer_len} \
+    reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} \
+    +reward_model.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \
+    +reward_model.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \
+    +reward_model.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \
+    +reward_model.reward_kwargs.overlong_buffer_cfg.log=False \
+    +reward_model.reward_kwargs.max_resp_len=${max_response_length} \
+    trainer.logger=['console','wandb'] \
+    trainer.project_name="${project_name}" \
+    trainer.experiment_name="${exp_name}-i${total_rollout_gpus}_t${total_train_gpus}_s${staleness_threshold}" \
+    trainer.val_before_train=True \
+    trainer.test_freq="${test_freq}" \
+    trainer.save_freq=-1 \
+    trainer.default_local_dir="${CKPTS_DIR}" \
+    trainer.resume_mode=auto \
+    trainer.nnodes="${n_nodes_train}" \
+    trainer.n_gpus_per_node="${n_gpus_training}" \
+    rollout.nnodes="${n_nodes_rollout}" \
+    rollout.n_gpus_per_node="${n_gpus_rollout}" \
+    rollout.total_rollout_steps="${total_rollout_steps}" \
+    rollout.test_freq=${test_freq} \
+    rollout.total_epochs=10 \
+    async_training.require_batches=${require_batches} \
+    async_training.staleness_threshold="${staleness_threshold}" \
+    async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \
+    async_training.partial_rollout="${partial_rollout}" \
+    async_training.use_rollout_log_probs=True
diff --git a/verl/workers/config/rollout.py b/verl/workers/config/rollout.py
index bea1bd4520d..0488b2c117b 100644
--- a/verl/workers/config/rollout.py
+++ b/verl/workers/config/rollout.py
@@ -207,6 +207,8 @@ class RolloutConfig(BaseConfig):
 
     enable_rollout_routing_replay: bool = False
 
+    enable_sleep_mode: bool = True
+
     def __post_init__(self):
         """Validate the rollout config"""
         if self.expert_parallel_size > 1:
diff --git a/verl/workers/rollout/vllm_rollout/vllm_async_server.py b/verl/workers/rollout/vllm_rollout/vllm_async_server.py
index 0dc079c22e2..0b121ecc8c0 100644
--- a/verl/workers/rollout/vllm_rollout/vllm_async_server.py
+++ b/verl/workers/rollout/vllm_rollout/vllm_async_server.py
@@ -254,6 +254,13 @@ async def launch_server(self, master_address: str = None, master_port: int = Non
             max_new_tokens=self.config.response_length,
         )
         logger.info(f"override_generation_config: {override_generation_config}")
+
+        logger.info(f"enable_sleep_mode: {self.config.enable_sleep_mode}")
+        if not self.config.enable_sleep_mode:
+            from verl.utils.device import set_expandable_segments
+
+            set_expandable_segments(True)
+
         quantization = self.config.quantization
         if quantization is not None:
             if quantization == "fp8":
@@ -279,7 +286,7 @@ async def launch_server(self, master_address: str = None, master_port: int = Non
             "enable_chunked_prefill": self.config.enable_chunked_prefill,
             "max_num_batched_tokens": self.config.max_num_batched_tokens,
             "enable_prefix_caching": self.config.enable_prefix_caching,
-            "enable_sleep_mode": True,
+            "enable_sleep_mode": self.config.enable_sleep_mode,
             "disable_custom_all_reduce": True,
             "enforce_eager": self.config.enforce_eager,
             "gpu_memory_utilization": self.config.gpu_memory_utilization,