diff --git a/examples/deepeyes_v2_agentic/run_deepeyes_v2_agentic_klx.sh b/examples/deepeyes_v2_agentic/run_deepeyes_v2_agentic_klx.sh
new file mode 100644
index 000000000..530d5bd34
--- /dev/null
+++ b/examples/deepeyes_v2_agentic/run_deepeyes_v2_agentic_klx.sh
@@ -0,0 +1,318 @@
+#!/bin/bash
+# Copyright (c) 2026 Relax Authors. All Rights Reserved.
+
+set -ex
+set -o pipefail
+now=$(date "+%Y-%m-%d-%H:%M:%S")
+echo "当前时间: $now"
+
+###############################################################################
+#                                 klx args                                    #
+###############################################################################
+export WANDB_API_KEY="${WANDB_API_KEY:=YOUR-KEY}"
+export WORKDIR="${WORKDIR:-/workspace}"
+export MODEL_DIR="${MODEL_DIR:-/workspace/deepeyes}"
+export DATA_DIR="${DATA_DIR:-/workspace/deepeyes}"
+export SAVE_DIR="${SAVE_DIR:-/workspace/deepeyes}"
+export PROJECT_NAME=deepeyes_v2_agentic
+ 
+export MEGATRON=${WORKDIR}/Megatron-LM
+
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export RL_MODEL_TYPE="Qwen36_35B_multimodal"
+export XMLIR_USE_HYDRA_LINEAR=${XMLIR_USE_HYDRA_LINEAR:-1}
+export XMLIR_ENABLE_FAST_FC=${XMLIR_ENABLE_FAST_FC:-1}
+export XMLIR_MATMUL_FAST_MODE=${XMLIR_MATMUL_FAST_MODE:-1}
+export XMLIR_MEMCPY_RETRY_SYNC=${XMLIR_MEMCPY_RETRY_SYNC:-true}
+
+export NCCL_SOCKET_IFNAME=${NCCL_SOCKET_IFNAME:-eth0}
+export GLOO_SOCKET_IFNAME=${GLOO_SOCKET_IFNAME:-eth0}
+export TP_SOCKET_IFNAME=${TP_SOCKET_IFNAME:-eth0}
+export BKCL_RDMA_NICS=${BKCL_RDMA_NICS:-bond0,bond1,bond2,bond3,bond4,bond5,bond6,bond7}
+
+unset http_proxy
+unset https_proxy
+
+###############################################################################
+#                                 ENVIRONMENT                                 #
+###############################################################################
+
+TIMESTAMP=$(date "+%Y-%m-%d-%H:%M:%S")
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)"
+# Auto-source env.sh if present (gitignored, machine-specific overrides).
+# shellcheck source=/dev/null
+[ -f "${SCRIPT_DIR}/env.sh" ] && source "${SCRIPT_DIR}/env.sh"
+
+if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then
+    source "${SCRIPT_DIR}/../../scripts/entrypoint/local.sh"
+fi
+source "${MODEL_CONFIG_DIR}/qwen36-35B-A3B.sh"
+
+###############################################################################
+#                                    DIRS                                     #
+###############################################################################
+
+PROJECT_NAME="${PROJECT_NAME:=Relax/dev/deepeyes-v2}"
+EXP_NAME="qwen36-35B-A3B-deepeyes-v2-agentic-${TIMESTAMP}"
+
+if [ -z "${MODEL_DIR:-}" ] || [ -z "${DATA_DIR:-}" ] || [ -z "${SAVE_DIR:-}" ]; then
+    echo "ERROR: MODEL_DIR, DATA_DIR, and SAVE_DIR must be set."
+    echo "Example: MODEL_DIR=/path/to/models DATA_DIR=/path/to/data SAVE_DIR=/path/to/save bash $0"
+    exit 1
+fi
+mkdir -p ${SAVE_DIR}
+
+###############################################################################
+#                             STARTUP CLEANUP                                 #
+###############################################################################
+# Sessions that die by SIGKILL / OOM / uncaught crash never run
+# ApptainerJupyterSession.close(), so their /tmp/relax-apptainer-* dirs
+# (kernel conn files + bind-mounted imgs/inputs holding base64-decoded
+# dataset images) leak. Over 2000 rollouts × 32 batch × 8 samples ≈ 512K
+# sessions, even a 1% leak fills the container disk and triggers eviction.
+# mtime > 240 min is well above any single session's max wall-clock (bounded
+# by max_turns × code_timeout_s ≈ tens of minutes), so live sessions are
+# never touched.
+find /tmp -maxdepth 1 -name 'relax-apptainer-*' -mmin +240 -exec rm -rf {} + 2>/dev/null || true
+# Per-run agent log dirs accumulate one file per session; keep 3 days.
+find "${SCRIPT_DIR}/log/agent" -maxdepth 1 -type d -mtime +3 -exec rm -rf {} + 2>/dev/null || true
+
+# SIF path under DATA_DIR layout; user may override APPTAINER_IMAGE_PATH to
+# point at a shared NFS copy elsewhere.
+export APPTAINER_IMAGE_PATH="${APPTAINER_IMAGE_PATH:-${DATA_DIR}/sif/deepeyes_v2_kernel.sif}"
+if [ ! -f "${APPTAINER_IMAGE_PATH}" ]; then
+    echo "ERROR: SIF not found at ${APPTAINER_IMAGE_PATH}."
+    echo "Run: DATA_DIR=${DATA_DIR} bash ${SCRIPT_DIR}/scripts/prepare.sh"
+    exit 1
+fi
+
+###############################################################################
+#                              JUDGE MODEL API                                #
+###############################################################################
+
+source "${SCRIPT_DIR}/sglang_judge_service_klx.sh"
+
+###############################################################################
+#                                  MODEL CONFIG                               #
+###############################################################################
+
+CKPT_ARGS=(
+    --hf-checkpoint ${MODEL_DIR}/Qwen3.6-35B-A3B
+    --ref-load ${MODEL_DIR}/Qwen3.6-35B-A3B
+    --warm-hf-checkpoint-page-cache
+    --save ${SAVE_DIR}/Qwen3.6-35B-A3B-Checkpoint_v2
+    --megatron-to-hf-mode bridge
+    --save-interval 200
+    --max-actor-ckpt-to-keep 1
+)
+
+###############################################################################
+#                                  DATASETS                                   #
+###############################################################################
+
+# Layout produced by scripts/prepare.sh:
+#   ${DATA_DIR}/data/{perception_all_*,reason,search,vstar_test}.parquet
+#   ${DATA_DIR}/sif/deepeyes_v2_kernel.sif
+TRAIN_FILES=(
+    "'${DATA_DIR}/data/perception_all_1.parquet@[0:5000]'"
+    "'${DATA_DIR}/data/reason.parquet@[0:5000]'"
+)
+TEST_FILES=("${DATA_DIR}/data/vstar_test.parquet@[0:256]")
+PROMPT_SET="[$(IFS=,; echo "${TRAIN_FILES[*]}")]"
+
+###############################################################################
+#                               ROLLOUT CONFIG                                #
+###############################################################################
+
+NUM_ROLLOUT="${NUM_ROLLOUT:=200}"
+
+# Sandbox env vars propagated into every Ray worker so the per-session
+# agent process can find apptainer / search cache.
+# SANDBOX_CONFIG_PATH is required — the agent reads it in _build_executor
+# to find the apptainer backend YAML config (image path, bind paths, etc).
+EXTRA_ENV_VARS_JSON="\"SANDBOX_BACKEND\": \"apptainer_jupyter\",
+    \"SANDBOX_CONFIG_PATH\": \"${SCRIPT_DIR}/apptainer_env/apptainer_config.yaml\",
+    \"APPTAINER_IMAGE_PATH\": \"${APPTAINER_IMAGE_PATH}\",
+    \"DEEPEYES_V2_SEARCH_CACHE_PATHS\": \"${DEEPEYES_V2_SEARCH_CACHE_PATHS:-}\",
+    \"DEEPEYES_JUDGE_BASE_URL\": \"${DEEPEYES_JUDGE_BASE_URL:-}\",
+    \"DEEPEYES_JUDGE_MODELS\": \"${DEEPEYES_JUDGE_MODELS:-}\",
+    \"DEEPEYES_JUDGE_API_KEY\": \"${DEEPEYES_JUDGE_API_KEY:-}\",
+    \"XMLIR_ENABLE_H2D_SSE_COPY\": \"${XMLIR_ENABLE_H2D_SSE_COPY:-1}\",
+    \"USE_CAST_FC_FUSION\": \"${USE_CAST_FC_FUSION:-1}\""
+source "${SCRIPT_DIR}/../../scripts/entrypoint/runtime-env-klx.sh"
+
+ROLLOUT_ARGS=(
+    --prompt-data "${PROMPT_SET}"
+    --input-key prompt
+    --label-key reward_model
+    --multimodal-keys '{"image":"images"}'
+    --reward-key score
+    --metadata-key extra_info
+    --custom-rm-path examples.deepeyes_v2_agentic.reward_deepeyes_v2.reward_func
+    --use-agentic-rollout
+    --agent-command ". ${SCRIPT_DIR}/run_agent_app.sh"
+    --agent-cwd "${SCRIPT_DIR}"
+    # Per-run agent log dir: every session's stdout/stderr is tee'd to
+    # ${dir}/${session_id}.log (run_agent_app.sh). Successful AND failed
+    # sessions are both kept, which is what makes hang/timeout diagnosis
+    # possible — Relax's own tmpdir-based capture drops both.
+    --agent-env "AGENT_DEBUG_LOG_DIR=${SCRIPT_DIR}/log/agent/${TIMESTAMP}"
+    # 30-min default is too generous: normal sessions take 1-3 min, a
+    # zombie session still burning chat completions after 10 min is
+    # ~always doomed. Faster session-level SIGKILL clears prepare-gate
+    # IR backlog quicker so new groups actually get served.
+    --num-rollout ${NUM_ROLLOUT}
+    --rollout-batch-size ${ROLLOUT_BATCH_SIZE:-32}
+    --micro-batch-size 1
+    --n-samples-per-prompt ${N_SAMPLES_PER_PROMPT:-8}
+    --rollout-max-context-len 16384
+    --rollout-max-response-len 4096
+    --rollout-max-prompt-len 4096
+    --rollout-temperature 1
+    --global-batch-size 256
+    --rollout-shuffle
+    --use-streaming-dataset
+    --agentic-prepare-pool-size 0
+)
+
+###############################################################################
+#                                EVAL CONFIG                                  #
+###############################################################################
+
+EVAL_ARGS=(
+    --skip-eval-before-train
+    --eval-interval 500
+    --eval-prompt-data vstar ${TEST_FILES}
+    --n-samples-per-eval-prompt 8
+    --eval-max-response-len 4096
+    --eval-top-p 0.7
+    --agentic-eval-prepare-pool-size 32
+)
+
+###############################################################################
+#                              ALGORITHM CONFIG                               #
+###############################################################################
+
+GRPO_ARGS=(
+    --advantage-estimator grpo
+    --kl-loss-coef 0.00
+    --kl-loss-type low_var_kl
+    --entropy-coef 0.00
+    --eps-clip 0.2
+    --eps-clip-high 0.28
+    --eps-clip-c 3
+    --use-tis
+)
+
+###############################################################################
+#                              OPTIMIZER CONFIG                               #
+###############################################################################
+
+OPTIMIZER_ARGS=(
+    --optimizer adam
+    --lr 1e-6
+    --lr-decay-style constant
+    --weight-decay 0.1
+    --adam-beta1 0.9
+    --adam-beta2 0.98
+    --optimizer-cpu-offload
+    --overlap-cpu-optimizer-d2h-h2d
+    --use-precision-aware-optimizer
+    --no-rope-fusion
+)
+
+###############################################################################
+#                               SGLANG CONFIG                                 #
+###############################################################################
+
+SGLANG_ARGS=(
+    --rollout-num-gpus-per-engine 2
+    --sglang-mem-fraction-static 0.7
+    --sglang-disable-custom-all-reduce
+    --sglang-page-size 64
+    --sglang-attention-backend kunlun
+    --sglang-disable-radix-cache
+    --sglang-max-running-requests 256
+    # --sglang-disable-cuda-graph
+    --sglang-cuda-graph-bs 1 2 4 8 $(seq 16 8 256)
+    --sglang-router-policy round_robin
+
+    --sglang-mm-attention-backend fa3
+    --sglang-mm-enable-dp-encoder
+)
+
+###############################################################################
+#                               LOGGING CONFIG                                #
+###############################################################################
+
+LOG_ARGS=(
+    --tb-experiment-name deepeyes_v2_agentic-klx-${now}
+    --use-wandb
+    --wandb-project ${PROJECT_NAME}
+    --wandb-group deepeyes_v2_agentic-klx-${now}
+    --wandb-key ${WANDB_API_KEY}
+    --disable-wandb-random-suffix
+    --no-use-metrics-service
+)
+
+###############################################################################
+#                              MEGATRON CONFIG                                #
+###############################################################################
+
+MEGATRON_ARGS=(
+    --tensor-model-parallel-size 4
+    --sequence-parallel
+    --pipeline-model-parallel-size 1
+    --context-parallel-size 1
+    --expert-model-parallel-size 8
+    --expert-tensor-parallel-size 1
+    --recompute-granularity full
+    --recompute-method uniform
+    --recompute-num-layers 1
+    --max-tokens-per-gpu 16384
+    --attention-dropout 0.0
+    --hidden-dropout 0.0
+    --accumulate-allreduce-grads-in-fp32
+    --attention-softmax-in-fp32
+    --attention-backend flash
+    --use-dynamic-batch-size
+
+    --moe-flex-dispatcher-backend deepep
+    --moe-token-dispatcher-type flex
+    --moe-grouped-gemm true
+)
+
+###############################################################################
+#                              RESOURCE CONFIG                                #
+###############################################################################
+
+RAY_RESOURCE_ARGS=(
+    --resource '{"actor": [1, 8], "rollout": [1, 8]}'
+    --max-staleness 0
+    --num-data-storage-units 1
+    # --use-health-check
+    --colocate
+)
+
+###############################################################################
+#                                 LAUNCH JOB                                  #
+###############################################################################
+
+mkdir -p logs
+
+ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \
+    --runtime-env-json "${RUNTIME_ENV_JSON}" \
+    -- python3 relax/entrypoints/train.py \
+    --selective-offload \
+    "${RAY_RESOURCE_ARGS[@]}" \
+    "${MODEL_ARGS[@]}" \
+    "${CKPT_ARGS[@]}" \
+    "${ROLLOUT_ARGS[@]}" \
+    "${GRPO_ARGS[@]}" \
+    "${OPTIMIZER_ARGS[@]}" \
+    "${SGLANG_ARGS[@]}" \
+    "${LOG_ARGS[@]}" \
+    "${MEGATRON_ARGS[@]}" \
+    "${EVAL_ARGS[@]}" \
+    2>&1 | tee logs/${EXP_NAME}.log
diff --git a/examples/deepeyes_v2_agentic/sglang_judge_service_klx.sh b/examples/deepeyes_v2_agentic/sglang_judge_service_klx.sh
new file mode 100644
index 000000000..d01f263c1
--- /dev/null
+++ b/examples/deepeyes_v2_agentic/sglang_judge_service_klx.sh
@@ -0,0 +1,141 @@
+#!/bin/bash
+# SGLang Judge 服务：脚本启动时自动部署，结束时自动终止
+# 用法: source "$(dirname "${BASH_SOURCE[0]}")/sglang_judge_service.sh"
+# 依赖: MODEL_DIR, TIMESTAMP 需在 source 前已定义
+
+# 设置默认端口，确保变量有初始值
+SGLANG_JUDGE_PORT=${SGLANG_JUDGE_PORT:-30000}
+SGLANG_JUDGE_MODEL_PATH="${MODEL_DIR}/Qwen2.5-1.5B-Instruct"
+SGLANG_JUDGE_PID=""
+LOG_FILE="logs/sglang_judge_${TIMESTAMP}.log"
+
+cleanup_sglang_judge() {
+    if [ -n "$SGLANG_JUDGE_PID" ] && kill -0 "$SGLANG_JUDGE_PID" 2>/dev/null; then
+        echo "Stopping sglang judge service (PID: $SGLANG_JUDGE_PID)..."
+        kill -9 "$SGLANG_JUDGE_PID" 2>/dev/null || true
+        echo "Sglang judge service stopped successfully."
+    fi
+}
+
+trap cleanup_sglang_judge EXIT
+
+# 检查必要依赖是否存在
+if ! command -v curl &> /dev/null; then
+    echo "Error: curl is required but not installed."
+    exit 1
+fi
+
+# 检查模型路径是否存在
+if [ ! -d "$SGLANG_JUDGE_MODEL_PATH" ]; then
+    echo "Error: Model path not found - $SGLANG_JUDGE_MODEL_PATH"
+    exit 1
+fi
+
+mkdir -p logs || { echo "Error: Failed to create logs directory"; exit 1; }
+
+# 启动 SGLang 服务
+echo "Starting sglang judge service on port $SGLANG_JUDGE_PORT..."
+echo "Model path: $SGLANG_JUDGE_MODEL_PATH"
+echo "Log file: $LOG_FILE"
+python -m sglang.launch_server \
+    --model-path "$SGLANG_JUDGE_MODEL_PATH" \
+    --port "$SGLANG_JUDGE_PORT" \
+    --api-key "EMPTY" \
+    --mem-fraction-static 0.05 \
+    --disable-cuda-graph \
+    --attention-backend kunlun \
+    --max-running-requests 32 \
+    > "$LOG_FILE" 2>&1 &
+
+SGLANG_JUDGE_PID=$!
+if [ -z "$SGLANG_JUDGE_PID" ]; then
+    echo "Error: Failed to get PID for sglang judge service"
+    exit 1
+fi
+echo "Sglang judge service started with PID: $SGLANG_JUDGE_PID"
+
+# 等待服务就绪
+wait_for_sglang_ready() {
+    local max_attempts=60       # 最大等待次数（总计5分钟）
+    local attempt=1
+    local retry_interval=5      # 重试间隔（秒）
+    local url="http://127.0.0.1:${SGLANG_JUDGE_PORT}/health"
+
+    echo "Waiting for sglang judge service to be ready (max wait: $((max_attempts * retry_interval)) seconds)..."
+
+    # 健康检查
+    while [ $attempt -le $max_attempts ]; do
+        local http_status
+        http_status=$(curl -s -o /dev/null -w "%{http_code}" --max-time 2 "$url" 2>/dev/null || echo "000")
+
+        if [ -z "$http_status" ]; then
+            http_status="000"
+        fi
+
+        if [ "$http_status" = "200" ] || [ "$http_status" = "204" ]; then
+            echo "Sglang judge service is ready (health check HTTP 200)."
+            return 0
+        fi
+
+        # 仅在非最后一次尝试时输出重试信息
+        if [ $attempt -lt $max_attempts ]; then
+            echo "  Attempt $attempt/$max_attempts: Waiting for judge model service (HTTP status: $http_status), retrying in ${retry_interval}s..."
+        fi
+
+        sleep $retry_interval
+        attempt=$((attempt + 1))
+    done
+
+    # 超时处理
+    echo "Error: Sglang judge service failed to start within timeout (${max_attempts} attempts)."
+    cleanup_sglang_judge
+    return 1
+}
+
+# 执行等待逻辑，失败则退出
+if ! wait_for_sglang_ready; then
+    echo "Aborting due to sglang judge service startup failure."
+    exit 1
+fi
+
+
+export DEEPEYES_JUDGE_API_KEY="EMPTY"
+export DEEPEYES_JUDGE_BASE_URL="http://127.0.0.1:${SGLANG_JUDGE_PORT}/v1"
+export DEEPEYES_JUDGE_MODELS="Qwen2.5-1.5B-Instruct"
+
+if [ -n "${RUNTIME_ENV_JSON:-}" ]; then
+    json_escape() {
+        local value="${1:-}"
+        value=${value//\\/\\\\}
+        value=${value//\"/\\\"}
+        value=${value//$'\n'/\\n}
+        value=${value//$'\r'/\\r}
+        value=${value//$'\t'/\\t}
+        printf '%s' "$value"
+    }
+
+    runtime_env_prefix="${RUNTIME_ENV_JSON%$'\n}\n}'}"
+    export RUNTIME_ENV_JSON="${runtime_env_prefix},
+   \"DEEPEYES_JUDGE_API_KEY\": \"$(json_escape "${DEEPEYES_JUDGE_API_KEY}")\",
+   \"DEEPEYES_JUDGE_BASE_URL\": \"$(json_escape "${DEEPEYES_JUDGE_BASE_URL}")\",
+   \"DEEPEYES_JUDGE_MODELS\": \"$(json_escape "${DEEPEYES_JUDGE_MODELS}")\"
+}
+}"
+fi
+
+ray() {
+    if [ "$1" = "job" ] && [ "$2" = "submit" ] && [ -n "${RUNTIME_ENV_JSON:-}" ]; then
+        local arg=""
+        for arg in "$@"; do
+            if [ "$arg" = "--runtime-env-json" ] || [[ "$arg" == --runtime-env-json=* ]]; then
+                command ray "$@"
+                return
+            fi
+        done
+        command ray job submit ${RAY_NO_WAIT:+--no-wait} --runtime-env-json="${RUNTIME_ENV_JSON}" "${@:3}"
+        return
+    fi
+    command ray "$@"
+}
+
+echo "Sglang judge service is fully ready for use."