diff --git a/PyTorch/built-in/rl/VeRL_for_PyTorch/recipe/dapo/src/config/dapo_trainer.yaml b/PyTorch/built-in/rl/VeRL_for_PyTorch/recipe/dapo/src/config/dapo_trainer.yaml
index bcafe90f69122a9ef64cf37ebf02b9929d64d587..d06566950b257ef8f4202eff2b0ecde1c7360101 100644
--- a/PyTorch/built-in/rl/VeRL_for_PyTorch/recipe/dapo/src/config/dapo_trainer.yaml
+++ b/PyTorch/built-in/rl/VeRL_for_PyTorch/recipe/dapo/src/config/dapo_trainer.yaml
@@ -1,3 +1,11 @@
+hydra:
+  searchpath:
+    - file://verl/trainer/config
+
+defaults:
+  - ppo_trainer
+  - _self_
+
 data:
   tokenizer: null
   train_files: ~/data/rlhf/gsm8k/train.parquet
diff --git a/PyTorch/built-in/rl/VeRL_for_PyTorch/test/runtime_env_32b.yaml b/PyTorch/built-in/rl/VeRL_for_PyTorch/test/runtime_env_32b.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c3d76cfb0a5d1212b4b68e9dbae03ff7d6012063
--- /dev/null
+++ b/PyTorch/built-in/rl/VeRL_for_PyTorch/test/runtime_env_32b.yaml
@@ -0,0 +1,5 @@
+working_dir: ./
+excludes: ["/.git/"]
+env_vars:
+  HCCL_CONNECT_TIMEOUT: "1500"
+  HCCL_EXEC_TIMEOUT: "1500"
\ No newline at end of file
diff --git a/PyTorch/built-in/rl/VeRL_for_PyTorch/test/train_qwen2_5_32b_instruct_DAPO_performance_32p.sh b/PyTorch/built-in/rl/VeRL_for_PyTorch/test/train_qwen2_5_32b_instruct_DAPO_performance_32p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8df492ae9708b0192206f40da77e0b49e9048b0e
--- /dev/null
+++ b/PyTorch/built-in/rl/VeRL_for_PyTorch/test/train_qwen2_5_32b_instruct_DAPO_performance_32p.sh
@@ -0,0 +1,143 @@
+#!/usr/bin/env bash
+set -euxo pipefail
+
+project_name='DAPO'
+exp_name='DAPO-Qwen2.5-32B-Instruct'
+
+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
+
+max_prompt_length=$((1024 * 2))
+max_response_length=$((1024 * 20))
+enable_overlong_buffer=True
+overlong_buffer_len=$((1024 * 4))
+overlong_penalty_factor=1.0
+
+loss_agg_mode="token-mean"
+
+enable_filter_groups=False
+filter_groups_metric=acc
+max_num_gen_batches=10
+train_prompt_bsz=16
+gen_prompt_bsz=$((train_prompt_bsz * 2))
+n_resp_per_prompt=16
+train_prompt_mini_bsz=1
+
+# Ray
+RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"}
+WORKING_DIR=${WORKING_DIR:-"${PWD}"}
+RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/test/runtime_env_32b.yaml"}
+NNODES=${NNODES:-2}
+# Paths
+RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
+MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/Qwen2.5-32B-Instruct"}
+CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
+TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/DAPO-Math-17k/data/dapo-math-17k.parquet"}
+TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/AIME-2024/data/aime-2024.parquet"}
+
+# Algorithm
+temperature=1.0
+top_p=1.0
+top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
+
+# Performance Related Parameter
+sp_size=8
+use_dynamic_bsz=True
+actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size))
+infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size))
+offload=True
+gen_tp=4
+
+ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \
+    --working-dir "${WORKING_DIR}" \
+    -- python3 -m recipe.dapo.src.main_dapo \
+    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.gen_batch_size=${gen_prompt_bsz} \
+    data.train_batch_size=${train_prompt_bsz} \
+    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.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 \
+    algorithm.filter_groups.enable=${enable_filter_groups} \
+    algorithm.filter_groups.max_num_gen_batches=${max_num_gen_batches} \
+    algorithm.filter_groups.metric=${filter_groups_metric} \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.use_dynamic_bsz=${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.model.override_config.attention_dropout=0. \
+    +actor_rollout_ref.model.override_config.embd_pdrop=0. \
+    +actor_rollout_ref.model.override_config.resid_pdrop=0. \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    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=${train_prompt_mini_bsz} \
+    actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.actor.grad_clip=1.0 \
+    actor_rollout_ref.actor.loss_agg_mode=${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.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=${temperature} \
+    actor_rollout_ref.rollout.val_kwargs.top_p=${top_p} \
+    actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \
+    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
+    actor_rollout_ref.rollout.val_kwargs.n=1 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \
+    actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \
+    actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \
+    reward_model.reward_manager=dapo \
+    reward_model.overlong_buffer.enable=${enable_overlong_buffer} \
+    reward_model.overlong_buffer.len=${overlong_buffer_len} \
+    reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} \
+    trainer.logger=['console'] \
+    trainer.project_name="${project_name}" \
+    trainer.experiment_name="${exp_name}" \
+    trainer.n_gpus_per_node=16 \
+    trainer.nnodes="${NNODES}" \
+    trainer.val_before_train=False \
+    trainer.test_freq=5 \
+    trainer.save_freq=-1 \
+    trainer.total_epochs=1 \
+    trainer.default_local_dir="${CKPTS_DIR}" \
+    trainer.resume_mode=auto \
+    data.shuffle=False \
+    actor_rollout_ref.actor.use_torch_compile=False \
+    actor_rollout_ref.ref.use_torch_compile=False \
+    actor_rollout_ref.actor.entropy_checkpointing=True \
+    actor_rollout_ref.ref.entropy_checkpointing=True \
+    actor_rollout_ref.actor.fsdp_config.forward_prefetch=True \
+    actor_rollout_ref.ref.fsdp_config.forward_prefetch=True \
+    actor_rollout_ref.actor.fsdp_config.backward_prefetch=BACKWARD_PRE \
+    actor_rollout_ref.ref.fsdp_config.backward_prefetch=BACKWARD_PRE \
+    actor_rollout_ref.actor.use_entropy_from_logits_with_chunking=True \
+    actor_rollout_ref.ref.use_entropy_from_logits_with_chunking=True
\ No newline at end of file
diff --git a/PyTorch/built-in/rl/VeRL_for_PyTorch/test/train_qwen2_5_7b_instruct_DAPO_performance_16p.sh b/PyTorch/built-in/rl/VeRL_for_PyTorch/test/train_qwen2_5_7b_instruct_DAPO_performance_16p.sh
index 62e43355f465824cb4f010724f65cbaa80b5a617..6376ac00931e707d2988a95e2400034141eea59f 100644
--- a/PyTorch/built-in/rl/VeRL_for_PyTorch/test/train_qwen2_5_7b_instruct_DAPO_performance_16p.sh
+++ b/PyTorch/built-in/rl/VeRL_for_PyTorch/test/train_qwen2_5_7b_instruct_DAPO_performance_16p.sh
@@ -132,12 +132,13 @@ ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \
     trainer.resume_mode=auto \
     data.shuffle=False \
     actor_rollout_ref.actor.use_torch_compile=False \
-    +actor_rollout_ref.ref.use_torch_compile=False \
-    +actor_rollout_ref.actor.entropy_checkpointing=True \
-    +actor_rollout_ref.ref.entropy_checkpointing=True \
-    +actor_rollout_ref.actor.fsdp_config.forward_prefetch=True \
-    +actor_rollout_ref.ref.fsdp_config.forward_prefetch=True \
-    +actor_rollout_ref.actor.fsdp_config.backward_prefetch=BACKWARD_PRE \
-    +actor_rollout_ref.ref.fsdp_config.backward_prefetch=BACKWARD_PRE \
-    +actor_rollout_ref.actor.use_entropy_from_logits_with_chunking=True \
-    +actor_rollout_ref.ref.use_entropy_from_logits_with_chunking=True
\ No newline at end of file
+    actor_rollout_ref.ref.use_torch_compile=False \
+    actor_rollout_ref.actor.entropy_checkpointing=True \
+    actor_rollout_ref.ref.entropy_checkpointing=True \
+    actor_rollout_ref.actor.fsdp_config.forward_prefetch=True \
+    actor_rollout_ref.ref.fsdp_config.forward_prefetch=True \
+    actor_rollout_ref.actor.fsdp_config.backward_prefetch=BACKWARD_PRE \
+    actor_rollout_ref.ref.fsdp_config.backward_prefetch=BACKWARD_PRE \
+    actor_rollout_ref.actor.use_entropy_from_logits_with_chunking=True \
+    actor_rollout_ref.ref.use_entropy_from_logits_with_chunking=True \
+    actor_rollout_ref.rollout.seed=1234
diff --git a/PyTorch/built-in/rl/VeRL_for_PyTorch/verl/trainer/config/ppo_trainer.yaml b/PyTorch/built-in/rl/VeRL_for_PyTorch/verl/trainer/config/ppo_trainer.yaml
index 59e9b9db570856ec34a2692c913ee845a14c20d3..85d5978759f7a8f853dc91721d627cf71f897882 100644
--- a/PyTorch/built-in/rl/VeRL_for_PyTorch/verl/trainer/config/ppo_trainer.yaml
+++ b/PyTorch/built-in/rl/VeRL_for_PyTorch/verl/trainer/config/ppo_trainer.yaml
@@ -63,9 +63,15 @@ actor_rollout_ref:
       param_offload: False
       optimizer_offload: False
       fsdp_size: -1
+      forward_prefetch: False
+      backward_prefetch: None
+    entropy_checkpointing: False
+    use_entropy_from_logits_with_chunking: False
   ref:
     fsdp_config:
       param_offload: False
+      forward_prefetch: False
+      backward_prefetch: None
       wrap_policy:
         # transformer_layer_cls_to_wrap: None
         min_num_params: 0
@@ -75,6 +81,8 @@ actor_rollout_ref:
     log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz}
     log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}
     ulysses_sequence_parallel_size: ${actor_rollout_ref.actor.ulysses_sequence_parallel_size} # sp size
+    entropy_checkpointing: False
+    use_entropy_from_logits_with_chunking: False
   rollout:
     name: vllm
     temperature: 1.0
@@ -104,6 +112,7 @@ actor_rollout_ref:
     do_sample: True
     # number of responses (i.e. num sample times)
     n: 1 # > 1 for grpo
+    seed: 0
     val_kwargs:
       # sampling parameters for validation
       top_k: -1 # 0 for hf rollout, -1 for vllm rollout
diff --git a/PyTorch/built-in/rl/VeRL_for_PyTorch/verl/workers/rollout/vllm_rollout/vllm_rollout_spmd.py b/PyTorch/built-in/rl/VeRL_for_PyTorch/verl/workers/rollout/vllm_rollout/vllm_rollout_spmd.py
index 0b5fbe3d372d6c05a1aae69b1d609bab5f79e3fa..f3584de66a737708cc5b42b471aa495e5a744643 100644
--- a/PyTorch/built-in/rl/VeRL_for_PyTorch/verl/workers/rollout/vllm_rollout/vllm_rollout_spmd.py
+++ b/PyTorch/built-in/rl/VeRL_for_PyTorch/verl/workers/rollout/vllm_rollout/vllm_rollout_spmd.py
@@ -126,6 +126,7 @@ class vLLMRollout(BaseRollout):
             enable_chunked_prefill=config.enable_chunked_prefill,
             enable_prefix_caching=True,
             trust_remote_code=trust_remote_code,
+            seed=config.seed
         )
 
         # Offload vllm model to reduce peak memory usage