diff --git a/examples/grpo_trainer/run_qwen3moe-30b_megatron_96gb.sh b/examples/grpo_trainer/run_qwen3moe-30b_megatron_96gb.sh
index 6937db5fcfa..1db311e28f2 100644
--- a/examples/grpo_trainer/run_qwen3moe-30b_megatron_96gb.sh
+++ b/examples/grpo_trainer/run_qwen3moe-30b_megatron_96gb.sh
@@ -30,7 +30,7 @@ train_prompt_mini_bsz=128
 train_ppo_micro_batch_size_per_gpu=2
 infer_ppo_micro_batch_size_per_gpu=2
 # Paths
-MODEL_PATH=Qwen/Qwen3-30B-A3B
+MODEL_PATH=Qwen/Qwen3-30B-A3B-Base
 
 RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
 TRAIN_FILE=$RAY_DATA_HOME/dataset/dapo-math-17k.parquet
diff --git a/recipe/dapo/dapo_ray_trainer.py b/recipe/dapo/dapo_ray_trainer.py
index a6a06192374..18995f67787 100644
--- a/recipe/dapo/dapo_ray_trainer.py
+++ b/recipe/dapo/dapo_ray_trainer.py
@@ -321,7 +321,7 @@ def fit(self):
 
                     rollout_corr_config = self.config.algorithm.get("rollout_correction", None)
                     if rollout_corr_config is not None and "rollout_log_probs" in batch.batch:
-                        batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch)
+                        batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch, rollout_corr_config)
                         # IS and off-policy metrics already have rollout_corr/ prefix
                         metrics.update(is_metrics)
 
diff --git a/recipe/fully_async_policy/megatron_utils.py b/recipe/fully_async_policy/megatron_utils.py
new file mode 100644
index 00000000000..9f5380f25c5
--- /dev/null
+++ b/recipe/fully_async_policy/megatron_utils.py
@@ -0,0 +1,99 @@
+# Copyright 2025 Bytedance Ltd. and/or its affiliates
+# Copyright 2025 Meituan Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+from megatron.core.distributed import DistributedDataParallel as DDP
+
+
+@torch.no_grad()
+def copy_megatron_model_to_cpu(models):
+    """
+    Copy Megatron model parameters to CPU memory (non-destructive copy).
+    Unlike offload_megatron_model_to_cpu which moves data, this function creates
+    independent copies on CPU while keeping GPU data intact.
+
+    Args:
+        models: List of model chunks (DDP-wrapped or unwrapped)
+
+    Returns:
+        dict: CPU state containing copied parameters and buffers
+    """
+    cpu_state = {}
+
+    for model_idx, model_chunk in enumerate(models):
+        if isinstance(model_chunk, DDP):
+            # Handle DDP-wrapped models
+            model_chunk_all_buffers = [model_chunk.buffers, model_chunk.expert_parallel_buffers]
+            buffer_states = []
+
+            for buffers in model_chunk_all_buffers:
+                buffer_list = []
+                for buffer in buffers:
+                    buffer_state = {}
+
+                    # Copy parameter data to CPU
+                    if buffer.param_data.storage().size() > 0:
+                        buffer_state["param_data"] = buffer.param_data.data.cpu().clone().pin_memory()
+
+                    buffer_list.append(buffer_state)
+                buffer_states.append(buffer_list)
+
+            cpu_state[f"model_chunk_{model_idx}"] = {"buffer_states": buffer_states, "is_ddp": True}
+        else:
+            # Handle non-DDP models (ref module)
+            model_state = {}
+            for name, param in model_chunk.named_parameters():
+                param_state = {"data": param.data.cpu().clone().pin_memory()}
+                model_state[name] = param_state
+
+            cpu_state[f"model_chunk_{model_idx}"] = {"model_state": model_state, "is_ddp": False}
+
+    return cpu_state
+
+
+@torch.no_grad()
+def restore_megatron_model_from_cpu(models, cpu_state):
+    """
+    Restore Megatron model parameters from CPU memory back to GPU.
+
+    Args:
+        models: List of model chunks to restore to
+        cpu_state: CPU state dict returned from copy_megatron_model_to_cpu
+    """
+    for model_idx, model_chunk in enumerate(models):
+        chunk_key = f"model_chunk_{model_idx}"
+        if chunk_key not in cpu_state:
+            continue
+
+        chunk_state = cpu_state[chunk_key]
+
+        if chunk_state["is_ddp"] and isinstance(model_chunk, DDP):
+            # Restore DDP buffers
+            model_chunk_all_buffers = [model_chunk.buffers, model_chunk.expert_parallel_buffers]
+            buffer_states = chunk_state["buffer_states"]
+
+            for buffers, buffer_list in zip(model_chunk_all_buffers, buffer_states, strict=False):
+                for buffer, buffer_state in zip(buffers, buffer_list, strict=False):
+                    # Restore parameter data
+                    if "param_data" in buffer_state:
+                        buffer.param_data.data.copy_(buffer_state["param_data"].to(buffer.param_data.device))
+
+        elif not chunk_state["is_ddp"] and not isinstance(model_chunk, DDP):
+            # Restore non-DDP models
+            model_state = chunk_state["model_state"]
+            for name, param in model_chunk.named_parameters():
+                if name in model_state:
+                    param_state = model_state[name]
+                    param.data.copy_(param_state["data"].to(param.device))
diff --git a/recipe/fully_async_policy/megatron_worker.py b/recipe/fully_async_policy/megatron_worker.py
index 7097ef64c2a..24bac66be77 100644
--- a/recipe/fully_async_policy/megatron_worker.py
+++ b/recipe/fully_async_policy/megatron_worker.py
@@ -21,6 +21,7 @@
 import torch.distributed
 from omegaconf import DictConfig
 
+from recipe.fully_async_policy.megatron_utils import copy_megatron_model_to_cpu, restore_megatron_model_from_cpu
 from verl.single_controller.base.decorator import Dispatch, register
 from verl.utils.device import (
     get_device_name,
@@ -89,6 +90,22 @@ def sync_rollout_weights(self):
             if self._is_rollout:
                 inference_model.load_weights([(key, tensor)])
 
+    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
+    def save_model_to_cpu(self, n):
+        if not hasattr(self, "cpu_saved_models"):
+            self.cpu_saved_models = {}
+        self.cpu_saved_models[n] = copy_megatron_model_to_cpu(self.actor.actor_module)
+
+    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
+    def restore_model_from_cpu(self, n):
+        if n in self.cpu_saved_models:
+            restore_megatron_model_from_cpu(self.actor.actor_module, self.cpu_saved_models[n])
+
+    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
+    def clear_cpu_model(self, n):
+        if n in self.cpu_saved_models:
+            del self.cpu_saved_models[n]
+
 
 class DetachActorWorker(DetachNcclSync):
     def _get_actor_params_generator(self):
diff --git a/recipe/fully_async_policy/ray_trainer.py b/recipe/fully_async_policy/ray_trainer.py
index ba6c1448ae6..c27851ddf19 100644
--- a/recipe/fully_async_policy/ray_trainer.py
+++ b/recipe/fully_async_policy/ray_trainer.py
@@ -430,7 +430,7 @@ def compute_old_log_prob(batch):
 
             rollout_corr_config = self.config.algorithm.get("rollout_correction", None)
             if rollout_corr_config is not None and "rollout_log_probs" in batch.batch:
-                batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch)
+                batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch, rollout_corr_config)
                 # IS and off-policy metrics already have rollout_corr/ prefix
                 metrics.update(is_metrics)
 
diff --git a/recipe/fully_async_policy/shell/grpo_30b_a3b_base_math_megatron_96_32.sh b/recipe/fully_async_policy/shell/grpo_30b_a3b_base_math_megatron_96_32.sh
new file mode 100644
index 00000000000..be9523f9e08
--- /dev/null
+++ b/recipe/fully_async_policy/shell/grpo_30b_a3b_base_math_megatron_96_32.sh
@@ -0,0 +1,230 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+project_name='GRPO-Qwen3-30b-Base-MATH'
+exp_name='GRPO-Qwen3-30b-Base-MATH-megatron-fully-async_96-32'
+
+RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
+MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-30B-A3B-Base"}
+CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
+TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
+TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}
+
+rollout_mode="async"
+rollout_name="vllm" # sglang or vllm
+if [ "$rollout_mode" = "async" ]; then
+    export VLLM_USE_V1=1
+    return_raw_chat="True"
+fi
+# Algorithm parameters
+adv_estimator=grpo
+
+use_kl_in_reward=False
+kl_coef=0.0
+use_kl_loss=True
+kl_loss_coef=0.001
+kl_loss_type=low_var_kl
+
+clip_ratio_low=0.2
+clip_ratio_high=0.28
+
+# Response length parameters
+max_prompt_length=$((1024 * 2))
+max_response_length=$((1024 * 8))
+enable_overlong_buffer=True
+overlong_buffer_len=$((1024 * 4))
+overlong_penalty_factor=1.0
+
+loss_agg_mode="token-mean"
+
+# Algorithm
+temperature=1.0
+top_p=1.0
+top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
+val_top_p=0.7
+
+# Performance Related Parameter
+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)))
+offload=True
+train_ppo_micro_batch_size_per_gpu=2
+infer_ppo_micro_batch_size_per_gpu=2
+
+optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.}
+
+COMMON_PP=${COMMON_PP:-1}
+COMMON_VPP=${COMMON_VPP:-null}
+COMMON_CP=${COMMON_CP:-2}
+COMMON_TP=${COMMON_TP:-2}
+COMMON_EP=${COMMON_EP:-8}
+COMMON_ETP=${COMMON_ETP:-1}
+
+TRAIN_TP=${TRAIN_TP:-$COMMON_TP}
+INFER_TP=${INFER_TP:-4}
+
+ACTOR_PP=${ACTOR_PP:-$COMMON_PP}
+ACTOR_VPP=${ACTOR_VPP:-$COMMON_VPP}
+ACTOR_CP=${ACTOR_CP:-$COMMON_CP}
+ACTOR_TP=${ACTOR_TP:-$TRAIN_TP}
+ACTOR_EP=${ACTOR_EP:-$COMMON_EP}
+ACTOR_ETP=${ACTOR_ETP:-$COMMON_ETP}
+ROLLOUT_TP=${ROLLOUT_TP:-$INFER_TP}
+REF_PP=${REF_PP:-$COMMON_PP}
+REF_VPP=${REF_VPP:-$COMMON_VPP}
+REF_CP=${REF_CP:-$COMMON_CP}
+REF_TP=${REF_TP:-$TRAIN_TP}
+REF_EP=${REF_EP:-$COMMON_EP}
+REF_ETP=${REF_ETP:-$COMMON_ETP}
+CRITIC_PP=${CRITIC_PP:-$COMMON_PP}
+CRITIC_VPP=${CRITIC_VPP:-$COMMON_VPP}
+CRITIC_CP=${CRITIC_CP:-$COMMON_CP}
+CRITIC_TP=${CRITIC_TP:-$TRAIN_TP}
+CRITIC_EP=${CRITIC_EP:-$COMMON_EP}
+CRITIC_ETP=${CRITIC_ETP:-$COMMON_ETP}
+RM_PP=${RM_PP:-$COMMON_PP}
+RM_VPP=${RM_VPP:-$COMMON_VPP}
+RM_CP=${RM_CP:-$COMMON_CP}
+RM_TP=${RM_TP:-$TRAIN_TP}
+RM_EP=${RM_EP:-$COMMON_EP}
+RM_ETP=${RM_ETP:-$COMMON_ETP}
+
+# install mbridge
+# pip3 install git+https://github.com/ISEEKYAN/mbridge
+USE_MBRIDGE=True
+USE_DIST_CKPT=False
+
+# Fully async specific parameters
+NNODES_ROLLOUT=${NNODES_ROLLOUT:-12}
+NNODES_TRAIN=${NNODES_TRAIN:-4}
+NGPUS_PER_NODE=${NGPUS_PER_NODE:-8}
+
+train_prompt_bsz=0
+gen_prompt_bsz=1
+n_resp_per_prompt=16
+train_prompt_mini_bsz=128
+total_rollout_steps=$(((512*400)))
+test_freq=20
+staleness_threshold=0.5
+trigger_parameter_sync_step=4
+require_batches=1
+partial_rollout=True
+
+python -m recipe.fully_async_policy.fully_async_main \
+    --config-path=config \
+    --config-name='fully_async_ppo_megatron_trainer.yaml'\
+    data.train_files="${TRAIN_FILE}" \
+    data.val_files="${TEST_FILE}" \
+    data.prompt_key=prompt \
+    data.truncation='left' \
+    data.max_prompt_length=${max_prompt_length} \
+    data.max_response_length=${max_response_length} \
+    data.train_batch_size=${train_prompt_bsz} \
+    data.return_raw_chat=${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.model.path="${MODEL_PATH}" \
+    actor_rollout_ref.actor.use_kl_loss=${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=${clip_ratio_high} \
+    actor_rollout_ref.actor.clip_ratio_c=10.0 \
+    +actor_rollout_ref.model.override_config.model_config.max_position_embeddings=$((max_prompt_length + max_response_length)) \
+    actor_rollout_ref.model.use_fused_kernels=False \
+    actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_ppo_micro_batch_size_per_gpu} \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
+    actor_rollout_ref.actor.optim.lr_decay_style='constant' \
+    actor_rollout_ref.actor.optim.weight_decay=0.1 \
+    actor_rollout_ref.actor.optim.lr_decay_steps=${total_rollout_steps} \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \
+    actor_rollout_ref.actor.megatron.use_mbridge=$USE_MBRIDGE \
+    actor_rollout_ref.actor.megatron.use_dist_checkpointing=$USE_DIST_CKPT \
+    actor_rollout_ref.actor.megatron.param_offload=${offload} \
+    actor_rollout_ref.actor.megatron.grad_offload=${offload} \
+    actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \
+    actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${ACTOR_TP} \
+    actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${ACTOR_PP} \
+    actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=${ACTOR_VPP} \
+    actor_rollout_ref.actor.megatron.context_parallel_size=${ACTOR_CP} \
+    actor_rollout_ref.actor.megatron.expert_model_parallel_size=${ACTOR_EP} \
+    actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ACTOR_ETP} \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type="flex" \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \
+    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=${INFER_TP} \
+    actor_rollout_ref.rollout.enable_chunked_prefill=True \
+    actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
+    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.rollout.name=${rollout_name} \
+    actor_rollout_ref.rollout.mode=${rollout_mode} \
+    actor_rollout_ref.rollout.calculate_log_probs=True \
+    actor_rollout_ref.hybrid_engine=False \
+    actor_rollout_ref.rollout.enforce_eager=True \
+    actor_rollout_ref.rollout.free_cache_engine=True \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \
+    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.ref.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \
+    actor_rollout_ref.ref.megatron.param_offload=${offload} \
+    actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${REF_TP} \
+    actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${REF_PP} \
+    actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=${REF_VPP} \
+    actor_rollout_ref.ref.megatron.context_parallel_size=${REF_CP} \
+    actor_rollout_ref.ref.megatron.expert_model_parallel_size=${REF_EP} \
+    actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${REF_ETP} \
+    reward_model.reward_manager=dapo \
+    +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','tensorboard'] \
+    trainer.project_name="${project_name}" \
+    trainer.experiment_name="${exp_name}" \
+    trainer.val_before_train=True \
+    trainer.save_freq=-1 \
+    trainer.total_epochs=10 \
+    trainer.resume_mode=auto \
+    trainer.log_val_generations=10 \
+    trainer.nnodes="${NNODES_TRAIN}" \
+    trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \
+    rollout.nnodes="${NNODES_ROLLOUT}" \
+    rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \
+    rollout.total_rollout_steps="${total_rollout_steps}" \
+    rollout.total_epochs=10 \
+    rollout.test_freq="${test_freq}" \
+    async_training.staleness_threshold="${staleness_threshold}" \
+    async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \
+    async_training.require_batches="${require_batches}" \
+    async_training.partial_rollout="${partial_rollout}" \
+    async_training.use_rollout_log_probs=True \
+
diff --git a/recipe/fully_async_policy/shell/grpo_30b_a3b_base_math_megatron_96_32_mis.sh b/recipe/fully_async_policy/shell/grpo_30b_a3b_base_math_megatron_96_32_mis.sh
new file mode 100644
index 00000000000..ba2d6e4680b
--- /dev/null
+++ b/recipe/fully_async_policy/shell/grpo_30b_a3b_base_math_megatron_96_32_mis.sh
@@ -0,0 +1,244 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+project_name='GRPO-Qwen3-30b-Base-MATH'
+exp_name='GRPO-Qwen3-30b-Base-MATH-megatron-fully-async_96-32'
+
+RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
+MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-30B-A3B-Base"}
+CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
+TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
+TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}
+
+rollout_mode="async"
+rollout_name="vllm" # sglang or vllm
+if [ "$rollout_mode" = "async" ]; then
+    export VLLM_USE_V1=1
+    return_raw_chat="True"
+fi
+# Algorithm parameters
+adv_estimator=grpo
+
+use_kl_in_reward=False
+kl_coef=0.0
+use_kl_loss=True
+kl_loss_coef=0.001
+kl_loss_type=low_var_kl
+
+clip_ratio_low=0.2
+clip_ratio_high=0.28
+
+# Response length parameters
+max_prompt_length=$((1024 * 2))
+max_response_length=$((1024 * 8))
+enable_overlong_buffer=True
+overlong_buffer_len=$((1024 * 4))
+overlong_penalty_factor=1.0
+
+loss_agg_mode="token-mean"
+
+# Algorithm
+temperature=1.0
+top_p=1.0
+top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
+val_top_p=0.7
+
+# Performance Related Parameter
+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)))
+offload=True
+train_ppo_micro_batch_size_per_gpu=2
+infer_ppo_micro_batch_size_per_gpu=2
+
+optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.}
+
+COMMON_PP=${COMMON_PP:-1}
+COMMON_VPP=${COMMON_VPP:-null}
+COMMON_CP=${COMMON_CP:-2}
+COMMON_TP=${COMMON_TP:-2}
+COMMON_EP=${COMMON_EP:-8}
+COMMON_ETP=${COMMON_ETP:-1}
+
+TRAIN_TP=${TRAIN_TP:-$COMMON_TP}
+INFER_TP=${INFER_TP:-4}
+
+ACTOR_PP=${ACTOR_PP:-$COMMON_PP}
+ACTOR_VPP=${ACTOR_VPP:-$COMMON_VPP}
+ACTOR_CP=${ACTOR_CP:-$COMMON_CP}
+ACTOR_TP=${ACTOR_TP:-$TRAIN_TP}
+ACTOR_EP=${ACTOR_EP:-$COMMON_EP}
+ACTOR_ETP=${ACTOR_ETP:-$COMMON_ETP}
+ROLLOUT_TP=${ROLLOUT_TP:-$INFER_TP}
+REF_PP=${REF_PP:-$COMMON_PP}
+REF_VPP=${REF_VPP:-$COMMON_VPP}
+REF_CP=${REF_CP:-$COMMON_CP}
+REF_TP=${REF_TP:-$TRAIN_TP}
+REF_EP=${REF_EP:-$COMMON_EP}
+REF_ETP=${REF_ETP:-$COMMON_ETP}
+CRITIC_PP=${CRITIC_PP:-$COMMON_PP}
+CRITIC_VPP=${CRITIC_VPP:-$COMMON_VPP}
+CRITIC_CP=${CRITIC_CP:-$COMMON_CP}
+CRITIC_TP=${CRITIC_TP:-$TRAIN_TP}
+CRITIC_EP=${CRITIC_EP:-$COMMON_EP}
+CRITIC_ETP=${CRITIC_ETP:-$COMMON_ETP}
+RM_PP=${RM_PP:-$COMMON_PP}
+RM_VPP=${RM_VPP:-$COMMON_VPP}
+RM_CP=${RM_CP:-$COMMON_CP}
+RM_TP=${RM_TP:-$TRAIN_TP}
+RM_EP=${RM_EP:-$COMMON_EP}
+RM_ETP=${RM_ETP:-$COMMON_ETP}
+
+# install mbridge
+# pip3 install git+https://github.com/ISEEKYAN/mbridge
+USE_MBRIDGE=True
+USE_DIST_CKPT=False
+
+# Fully async specific parameters
+NNODES_ROLLOUT=${NNODES_ROLLOUT:-12}
+NNODES_TRAIN=${NNODES_TRAIN:-4}
+NGPUS_PER_NODE=${NGPUS_PER_NODE:-8}
+
+train_prompt_bsz=0
+gen_prompt_bsz=1
+n_resp_per_prompt=16
+train_prompt_mini_bsz=128
+total_rollout_steps=$(((512*400)))
+test_freq=20
+staleness_threshold=0.5
+trigger_parameter_sync_step=4
+require_batches=1
+partial_rollout=True
+
+# Rollout Importance Sampling
+
+rollout_is=null
+rollout_rs=geometric
+rollout_rs_threshold=1.001
+rollout_rs_threshold_lower=0.999
+rollout_token_veto_threshold=1e-4
+
+python -m recipe.fully_async_policy.fully_async_main \
+    --config-path=config \
+    --config-name='fully_async_ppo_megatron_trainer.yaml'\
+    data.train_files="${TRAIN_FILE}" \
+    data.val_files="${TEST_FILE}" \
+    data.prompt_key=prompt \
+    data.truncation='left' \
+    data.max_prompt_length=${max_prompt_length} \
+    data.max_response_length=${max_response_length} \
+    data.train_batch_size=${train_prompt_bsz} \
+    data.return_raw_chat=${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} \
+    async_training.compute_prox_log_prob=True \
+    algorithm.rollout_correction.rollout_is=${rollout_is} \
+    algorithm.rollout_correction.rollout_rs=${rollout_rs} \
+    algorithm.rollout_correction.rollout_rs_threshold=${rollout_rs_threshold} \
+    algorithm.rollout_correction.rollout_rs_threshold_lower=${rollout_rs_threshold_lower} \
+    algorithm.rollout_correction.rollout_token_veto_threshold=${rollout_token_veto_threshold} \
+    actor_rollout_ref.model.path="${MODEL_PATH}" \
+    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.override_config.model_config.max_position_embeddings=$((max_prompt_length + max_response_length)) \
+    actor_rollout_ref.model.use_fused_kernels=False \
+    actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_ppo_micro_batch_size_per_gpu} \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
+    actor_rollout_ref.actor.optim.lr_decay_style='constant' \
+    actor_rollout_ref.actor.optim.weight_decay=0.1 \
+    actor_rollout_ref.actor.optim.lr_decay_steps=${total_rollout_steps} \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \
+    actor_rollout_ref.actor.megatron.use_mbridge=$USE_MBRIDGE \
+    actor_rollout_ref.actor.megatron.use_dist_checkpointing=$USE_DIST_CKPT \
+    actor_rollout_ref.actor.megatron.param_offload=${offload} \
+    actor_rollout_ref.actor.megatron.grad_offload=${offload} \
+    actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \
+    actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${ACTOR_TP} \
+    actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${ACTOR_PP} \
+    actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=${ACTOR_VPP} \
+    actor_rollout_ref.actor.megatron.context_parallel_size=${ACTOR_CP} \
+    actor_rollout_ref.actor.megatron.expert_model_parallel_size=${ACTOR_EP} \
+    actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ACTOR_ETP} \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type="flex" \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \
+    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=${INFER_TP} \
+    actor_rollout_ref.rollout.enable_chunked_prefill=True \
+    actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
+    actor_rollout_ref.rollout.temperature=${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=${top_k} \
+    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
+    actor_rollout_ref.rollout.val_kwargs.n=1 \
+    actor_rollout_ref.rollout.name=${rollout_name} \
+    actor_rollout_ref.rollout.mode=${rollout_mode} \
+    actor_rollout_ref.rollout.calculate_log_probs=True \
+    actor_rollout_ref.hybrid_engine=False \
+    actor_rollout_ref.rollout.enforce_eager=True \
+    actor_rollout_ref.rollout.free_cache_engine=True \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \
+    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.ref.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \
+    actor_rollout_ref.ref.megatron.param_offload=${offload} \
+    actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${REF_TP} \
+    actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${REF_PP} \
+    actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=${REF_VPP} \
+    actor_rollout_ref.ref.megatron.context_parallel_size=${REF_CP} \
+    actor_rollout_ref.ref.megatron.expert_model_parallel_size=${REF_EP} \
+    actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${REF_ETP} \
+    reward_model.reward_manager=dapo \
+    +reward_model.reward_kwargs.overlong_buffer_cfg.enable=${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=${max_response_length} \
+    trainer.logger=['console','tensorboard'] \
+    trainer.project_name="${project_name}" \
+    trainer.experiment_name="${exp_name}" \
+    trainer.val_before_train=True \
+    trainer.save_freq=-1 \
+    trainer.total_epochs=10 \
+    trainer.resume_mode=auto \
+    trainer.log_val_generations=10 \
+    trainer.nnodes="${NNODES_TRAIN}" \
+    trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \
+    rollout.nnodes="${NNODES_ROLLOUT}" \
+    rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \
+    rollout.total_rollout_steps="${total_rollout_steps}" \
+    rollout.total_epochs=10 \
+    rollout.test_freq="${test_freq}" \
+    async_training.staleness_threshold="${staleness_threshold}" \
+    async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \
+    async_training.require_batches="${require_batches}" \
+    async_training.partial_rollout="${partial_rollout}" \
+    async_training.use_rollout_log_probs=True \
+
diff --git a/recipe/one_step_off_policy/ray_trainer.py b/recipe/one_step_off_policy/ray_trainer.py
index 3f5ec30ac17..f5c1c4612f1 100644
--- a/recipe/one_step_off_policy/ray_trainer.py
+++ b/recipe/one_step_off_policy/ray_trainer.py
@@ -582,7 +582,7 @@ def fit(self):
 
                     rollout_corr_config = self.config.algorithm.get("rollout_correction", None)
                     if rollout_corr_config is not None and "rollout_log_probs" in batch.batch:
-                        batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch)
+                        batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch, rollout_corr_config)
                         # IS and off-policy metrics already have rollout_corr/ prefix
                         metrics.update(is_metrics)
 
diff --git a/verl/trainer/config/_generated_ppo_megatron_trainer.yaml b/verl/trainer/config/_generated_ppo_megatron_trainer.yaml
index fc121e3c2e0..a6b54eb7904 100644
--- a/verl/trainer/config/_generated_ppo_megatron_trainer.yaml
+++ b/verl/trainer/config/_generated_ppo_megatron_trainer.yaml
@@ -469,14 +469,15 @@ reward_model:
     use_mbridge: ${oc.select:actor_rollout_ref.actor.megatron.use_mbridge,False}
   load_weight: true
 algorithm:
-  rollout_is: null
-  rollout_is_threshold: 2.0
-  rollout_rs: null
-  rollout_rs_threshold: null
-  rollout_rs_threshold_lower: null
-  rollout_token_veto_threshold: null
-  bypass_old_logprob_for_rollout: false
-  use_pure_rollout_correction: false
+  rollout_correction:
+    rollout_is: null
+    rollout_is_threshold: 2.0
+    rollout_rs: null
+    rollout_rs_threshold: null
+    rollout_rs_threshold_lower: null
+    rollout_token_veto_threshold: null
+    bypass_old_logprob_for_rollout: false
+    use_pure_rollout_correction: false
   _target_: verl.trainer.config.AlgoConfig
   gamma: 1.0
   lam: 1.0
diff --git a/verl/trainer/config/ppo_megatron_trainer.yaml b/verl/trainer/config/ppo_megatron_trainer.yaml
index c126dc418ee..0815451d178 100644
--- a/verl/trainer/config/ppo_megatron_trainer.yaml
+++ b/verl/trainer/config/ppo_megatron_trainer.yaml
@@ -16,7 +16,7 @@ defaults:
   # Reward model config.
   - reward_model@reward_model: megatron_reward_model
   # Rollout correction config.
-  - algorithm@algorithm: rollout_correction
+  - algorithm@algorithm.rollout_correction: rollout_correction
   - _self_
 
 actor_rollout_ref:
diff --git a/verl/trainer/ppo/ray_trainer.py b/verl/trainer/ppo/ray_trainer.py
index 3ec59760a05..88cfd715f1c 100644
--- a/verl/trainer/ppo/ray_trainer.py
+++ b/verl/trainer/ppo/ray_trainer.py
@@ -1172,7 +1172,7 @@ def fit(self):
                         # This corrects for off-policy issues (policy mismatch, model staleness, etc.)
                         # Also computes off-policy diagnostic metrics (KL, PPL, etc.)
                         if rollout_corr_config is not None and "rollout_log_probs" in batch.batch:
-                            batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch)
+                            batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch, rollout_corr_config)
                             # IS and off-policy metrics already have rollout_corr/ prefix
                             metrics.update(is_metrics)