diff --git a/examples/flowgrpo_trainer/bagel/README.md b/examples/flowgrpo_trainer/bagel/README.md
index d50b1880c..57c78040c 100644
--- a/examples/flowgrpo_trainer/bagel/README.md
+++ b/examples/flowgrpo_trainer/bagel/README.md
@@ -127,6 +127,26 @@ checkpointing.  The understanding pathway (``moe_und``) is not a LoRA
 wrapper replacement but simply has ``requires_grad=False`` set by the
 ``configure_trainable_params`` hook.
 
+## V1 (sync) training
+
+A V1-trainer counterpart of the PickScore LoRA recipe is available, using
+TransferQueue + ReplayBuffer via `verl_omni.trainer.main_diffusion_v1`:
+
+```bash
+bash examples/flowgrpo_trainer/bagel/run_bagel_pickscore_lora_v1.sh
+```
+
+Same dataset preparation and config as the PickScore LoRA recipe above; the
+only differences are the entrypoint and:
+
+```text
+trainer.use_v1=true
+trainer.v1.trainer_mode=sync
+```
+
+See the [Diffusion V1 guide](../../../docs/start/diffusion_v1.md) for the
+general V1 trainer architecture (documented there using the SD3.5 recipe).
+
 ## Key differences from Qwen-Image
 
 | Aspect | Qwen-Image | BAGEL-7B-MoT |
diff --git a/examples/flowgrpo_trainer/bagel/run_bagel_pickscore_lora_v1.sh b/examples/flowgrpo_trainer/bagel/run_bagel_pickscore_lora_v1.sh
new file mode 100755
index 000000000..63c5de011
--- /dev/null
+++ b/examples/flowgrpo_trainer/bagel/run_bagel_pickscore_lora_v1.sh
@@ -0,0 +1,93 @@
+#!/usr/bin/env bash
+# Bagel LoRA RL (V1 trainer: TransferQueue + ReplayBuffer + sync mode), vllm_omni
+# rollout (FlowGRPO) with PickScore reward.
+#
+# V1 counterpart of run_bagel_pickscore_lora.sh via main_diffusion_v1
+# (trainer.v1.trainer_mode=sync).
+#
+# Prerequisite: preprocess the PickScore dataset for BAGEL:
+#   python examples/flowgrpo_trainer/data_process/bagel_pickscore.py \
+#       --model_path ~/models/ByteDance-Seed/BAGEL-7B-MoT \
+#       --input_dir ~/data/pickscore \
+#       --output_dir ~/data/pickscore/bagel
+#
+# Raw dataset (train.txt / test.txt) from:
+#   https://github.com/yifan123/flow_grpo/tree/main/dataset/pickscore
+set -x
+
+# Set WORKSPACE to any writable directory; defaults to $HOME
+WORKSPACE=${WORKSPACE:-$HOME}
+
+pickscore_train_path=$WORKSPACE/data/pickscore/bagel/train.parquet
+pickscore_test_path=$WORKSPACE/data/pickscore/bagel/test.parquet
+
+BAGEL_DEPLOY_CONFIG=${BAGEL_DEPLOY_CONFIG:-"$(dirname "$0")/bagel_deploy_config.yaml"}
+
+model_name=~/models/ByteDance-Seed/BAGEL-7B-MoT
+reward_function_path=verl_omni/utils/reward_score/pickscore_reward.py
+
+NUM_GPUS_ACTOR_ROLLOUT_REWARD=4
+ROLLOUT_TP=1
+
+ENGINE=vllm_omni
+
+# enable reward model on 0'th gpu, it is a temporary workaround
+export RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+
+python3 -m verl_omni.trainer.main_diffusion_v1 \
+    data.train_files=$pickscore_train_path \
+    data.val_files=$pickscore_test_path \
+    data.train_batch_size=48 \
+    data.max_prompt_length=256 \
+    data.trust_remote_code=True \
+    algorithm.global_std=False \
+    actor_rollout_ref.model.path=$model_name \
+    actor_rollout_ref.model.tokenizer_path=$model_name \
+    +actor_rollout_ref.model.architecture=OmniBagelForConditionalGeneration \
+    actor_rollout_ref.model.trust_remote_code=True \
+    actor_rollout_ref.model.lora_rank=64 \
+    actor_rollout_ref.model.lora_alpha=128 \
+    actor_rollout_ref.model.lora_dtype=float32 \
+    actor_rollout_ref.model.target_modules="['q_proj_moe_gen','k_proj_moe_gen','v_proj_moe_gen','o_proj_moe_gen','mlp_moe_gen.gate_proj','mlp_moe_gen.up_proj','mlp_moe_gen.down_proj']" \
+    actor_rollout_ref.model.fsdp_layer_prefixes="['layers.']" \
+    actor_rollout_ref.actor.optim.lr=1e-4 \
+    actor_rollout_ref.actor.optim.weight_decay=0.0001 \
+    actor_rollout_ref.actor.ppo_mini_batch_size=24 \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=12 \
+    actor_rollout_ref.actor.fsdp_config.param_offload=True \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \
+    actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 \
+    actor_rollout_ref.actor.diffusion_loss.clip_ratio=1e-5 \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=12 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=$ROLLOUT_TP \
+    actor_rollout_ref.rollout.name=$ENGINE \
+    actor_rollout_ref.rollout.n=16 \
+    actor_rollout_ref.rollout.agent.num_workers=$((NUM_GPUS_ACTOR_ROLLOUT_REWARD / ROLLOUT_TP)) \
+    actor_rollout_ref.rollout.load_format=safetensors \
+    actor_rollout_ref.rollout.layered_summon=True \
+    actor_rollout_ref.rollout.pipeline.num_inference_steps=15 \
+    actor_rollout_ref.rollout.pipeline.max_sequence_length=256 \
+    actor_rollout_ref.rollout.algo.noise_level=1.3 \
+    actor_rollout_ref.rollout.algo.sde_type="sde" \
+    actor_rollout_ref.rollout.algo.sde_window_size=2 \
+    actor_rollout_ref.rollout.algo.sde_window_range="[0,7]" \
+    actor_rollout_ref.rollout.val_kwargs.pipeline.num_inference_steps=50 \
+    actor_rollout_ref.rollout.val_kwargs.algo.noise_level=0.0 \
+    +actor_rollout_ref.rollout.engine_kwargs.vllm_omni.deploy_config=$BAGEL_DEPLOY_CONFIG \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=12 \
+    reward.num_workers=1 \
+    reward.custom_reward_function.path=$reward_function_path \
+    reward.custom_reward_function.name=compute_score_pickscore \
+    trainer.logger='["console", "wandb"]' \
+    trainer.project_name=flow_grpo \
+    trainer.experiment_name=bagel_pickscore_lora_v1 \
+    trainer.log_val_generations=8 \
+    trainer.val_before_train=False \
+    trainer.n_gpus_per_node=$NUM_GPUS_ACTOR_ROLLOUT_REWARD \
+    trainer.nnodes=1 \
+    trainer.save_freq=30 \
+    trainer.test_freq=30 \
+    trainer.total_epochs=15 \
+    trainer.total_training_steps=300 \
+    trainer.use_v1=true \
+    trainer.v1.trainer_mode=sync "$@"
diff --git a/tests/special_e2e/run_flowgrpo_bagel_pickscore_v1.sh b/tests/special_e2e/run_flowgrpo_bagel_pickscore_v1.sh
new file mode 100755
index 000000000..cc22cd1a3
--- /dev/null
+++ b/tests/special_e2e/run_flowgrpo_bagel_pickscore_v1.sh
@@ -0,0 +1,149 @@
+#!/usr/bin/env bash
+# FlowGRPO BAGEL PickScore LoRA e2e smoke test, V1 trainer (sync mode).
+# V1 counterpart of run_flowgrpo_bagel_pickscore.sh via main_diffusion_v1.
+#
+# Requires: vllm-omni
+#   Builds offline if missing:
+#     ~/models/tiny-random/BAGEL-MoT
+#     ~/models/tiny-random/PickScore
+set -euo pipefail
+
+REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
+cd "${REPO_ROOT}"
+
+NUM_GPUS=${NUM_GPUS:-4}
+MODEL_PATH=${MODEL_PATH:-${HOME}/models/tiny-random/BAGEL-MoT}
+PICKSCORE_PATH=${PICKSCORE_PATH:-${HOME}/models/tiny-random/PickScore}
+DATA_DIR=${DATA_DIR:-${HOME}/data/dummy_bagel_pickscore}
+dummy_train_path=${TRAIN_FILES:-${DATA_DIR}/train.parquet}
+dummy_test_path=${VAL_FILES:-${DATA_DIR}/test.parquet}
+TOTAL_TRAIN_STEPS=${TOTAL_TRAIN_STEPS:-2}
+BAGEL_DEPLOY_CONFIG=${BAGEL_DEPLOY_CONFIG:-"${REPO_ROOT}/examples/flowgrpo_trainer/bagel/bagel_deploy_config.yaml"}
+
+ENGINE=vllm_omni
+max_prompt_length=64
+
+# Smoke: prefer FA3 when available; fall back like the Qwen-Image e2e.
+ATTN_BACKEND=_flash_3_varlen_hub
+ROLLOUT_ATTN_BACKEND=FLASH_ATTN
+if ! python3 -c 'from verl_omni.utils.diffusion_attention import fa3_available; raise SystemExit(0 if fa3_available() else 1)' >/dev/null 2>&1; then
+    ATTN_BACKEND=native
+    ROLLOUT_ATTN_BACKEND=TORCH_SDPA
+fi
+
+_LEAK_FILE=$(mktemp)
+_LEAK_PID=""
+cleanup_leak_monitor() {
+    [[ -n "${_LEAK_PID}" ]] && kill "${_LEAK_PID}" 2>/dev/null || true
+    rm -f "${_LEAK_FILE}"
+}
+trap cleanup_leak_monitor EXIT
+
+start_leak_monitor() {
+    : > "${_LEAK_FILE}"
+    while true; do
+        if nvidia-smi -i 0 2>&1 | grep -q "vLLMOmniHttpServer"; then
+            echo "LEAK" >> "${_LEAK_FILE}"
+        fi
+        sleep 1
+    done &
+    _LEAK_PID=$!
+}
+
+check_leak_monitor() {
+    kill "${_LEAK_PID}" 2>/dev/null || true
+    _LEAK_PID=""
+    if grep -q "LEAK" "${_LEAK_FILE}" 2>/dev/null; then
+        echo ""
+        echo "FAIL: unexpected vLLMOmniHttpServer process(es) detected on GPU-0 —"
+        ray stop --force 2>/dev/null || true
+        exit 1
+    fi
+}
+
+n_resp_per_prompt=2
+micro_bsz_per_gpu=1
+micro_bsz=$((micro_bsz_per_gpu * NUM_GPUS))
+mini_bsz=${micro_bsz}
+train_batch_size=$((mini_bsz * n_resp_per_prompt))
+
+python3 tests/special_e2e/build_bagel_tiny_random.py --output-dir "${MODEL_PATH}"
+python3 tests/special_e2e/build_pickscore_tiny_random.py --output-dir "${PICKSCORE_PATH}"
+python3 tests/special_e2e/create_dummy_bagel_pickscore_data.py \
+    --local_save_dir "${DATA_DIR}" \
+    --model_path "${MODEL_PATH}" \
+    --train_size "${train_batch_size}" \
+    --val_size 4 \
+    --max_prompt_length "${max_prompt_length}"
+
+export RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+export PICKSCORE_PATH
+
+start_leak_monitor
+python3 -m verl_omni.trainer.main_diffusion_v1 \
+    data.train_files=${dummy_train_path} \
+    data.val_files=${dummy_test_path} \
+    data.train_batch_size=${train_batch_size} \
+    data.max_prompt_length=${max_prompt_length} \
+    data.trust_remote_code=True \
+    algorithm.global_std=False \
+    actor_rollout_ref.model.algorithm=flow_grpo \
+    actor_rollout_ref.model.path=${MODEL_PATH} \
+    actor_rollout_ref.model.tokenizer_path=${MODEL_PATH} \
+    +actor_rollout_ref.model.architecture=OmniBagelForConditionalGeneration \
+    actor_rollout_ref.model.trust_remote_code=True \
+    actor_rollout_ref.model.attn_backend=${ATTN_BACKEND} \
+    actor_rollout_ref.model.lora_rank=8 \
+    actor_rollout_ref.model.lora_alpha=16 \
+    actor_rollout_ref.model.lora_dtype=float32 \
+    actor_rollout_ref.model.target_modules="['q_proj_moe_gen','k_proj_moe_gen','v_proj_moe_gen','o_proj_moe_gen','mlp_moe_gen.gate_proj','mlp_moe_gen.up_proj','mlp_moe_gen.down_proj']" \
+    actor_rollout_ref.model.fsdp_layer_prefixes="['layers.']" \
+    actor_rollout_ref.actor.optim.lr=1e-4 \
+    actor_rollout_ref.actor.optim.weight_decay=0.0001 \
+    actor_rollout_ref.actor.ppo_mini_batch_size=${mini_bsz} \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${micro_bsz_per_gpu} \
+    actor_rollout_ref.actor.fsdp_config.param_offload=True \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \
+    actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 \
+    actor_rollout_ref.actor.diffusion_loss.clip_ratio=1e-5 \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${micro_bsz_per_gpu} \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
+    actor_rollout_ref.rollout.name=${ENGINE} \
+    actor_rollout_ref.rollout.rollout_attn_backend=${ROLLOUT_ATTN_BACKEND} \
+    actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
+    actor_rollout_ref.rollout.agent.num_workers=1 \
+    actor_rollout_ref.rollout.load_format=safetensors \
+    actor_rollout_ref.rollout.layered_summon=True \
+    actor_rollout_ref.rollout.pipeline.num_inference_steps=4 \
+    actor_rollout_ref.rollout.pipeline.height=256 \
+    actor_rollout_ref.rollout.pipeline.width=256 \
+    actor_rollout_ref.rollout.pipeline.max_sequence_length=${max_prompt_length} \
+    actor_rollout_ref.rollout.enforce_eager=True \
+    actor_rollout_ref.rollout.algo.noise_level=1.0 \
+    actor_rollout_ref.rollout.algo.sde_type="sde" \
+    actor_rollout_ref.rollout.algo.sde_window_size=2 \
+    actor_rollout_ref.rollout.algo.sde_window_range="[0,4]" \
+    actor_rollout_ref.rollout.val_kwargs.pipeline.num_inference_steps=4 \
+    actor_rollout_ref.rollout.val_kwargs.algo.noise_level=0.0 \
+    +actor_rollout_ref.rollout.engine_kwargs.vllm_omni.deploy_config=${BAGEL_DEPLOY_CONFIG} \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${micro_bsz_per_gpu} \
+    reward.num_workers=1 \
+    reward.custom_reward_function.path=tests/special_e2e/bagel_pickscore_reward.py \
+    reward.custom_reward_function.name=compute_score_pickscore \
+    trainer.logger=console \
+    trainer.project_name=verl-test \
+    trainer.experiment_name=flowgrpo-bagel-pickscore-v1-e2e \
+    trainer.log_val_generations=0 \
+    trainer.n_gpus_per_node=${NUM_GPUS} \
+    trainer.nnodes=1 \
+    trainer.val_before_train=False \
+    trainer.test_freq=-1 \
+    trainer.save_freq=-1 \
+    trainer.resume_mode=disable \
+    trainer.total_training_steps=${TOTAL_TRAIN_STEPS} \
+    trainer.use_v1=true \
+    trainer.v1.trainer_mode=sync \
+    "$@"
+check_leak_monitor
+
+echo "FlowGRPO BAGEL PickScore LoRA V1 (sync) e2e test passed (training completed successfully)."
diff --git a/verl_omni/pipelines/bagel_flow_grpo/diffusers_training_adapter.py b/verl_omni/pipelines/bagel_flow_grpo/diffusers_training_adapter.py
index e88e39f6e..b49d024fd 100644
--- a/verl_omni/pipelines/bagel_flow_grpo/diffusers_training_adapter.py
+++ b/verl_omni/pipelines/bagel_flow_grpo/diffusers_training_adapter.py
@@ -106,17 +106,17 @@ def _prompt_token_ids_to_batch(
         micro_batch: TensorDict,
         device: torch.device,
     ) -> tuple[torch.Tensor, torch.Tensor]:
-        """Pad BAGEL-native ``prompt_token_ids`` from the data pipeline.
+        """Pad BAGEL-native ``prompts`` token ids from the data pipeline.
 
         Args:
-            micro_batch: Batch containing pre-tokenized ``prompt_token_ids``,
+            micro_batch: Batch containing pre-tokenized ``prompts``,
                 one sequence per sample, already in BAGEL format.
             device: Target device for the padded tensors.
 
         Returns:
             ``(text_token_ids, text_attention_mask)`` with shape ``(B, max_len)``.
         """
-        prompt_token_ids = micro_batch["prompt_token_ids"]
+        prompt_token_ids = micro_batch["prompts"]
         if isinstance(prompt_token_ids, NonTensorStack):
             prompt_token_ids = [
                 tu.unwrap_non_tensor_data(prompt_token_ids[i]) for i in range(micro_batch.batch_size[0])