diff --git a/skyrl-train/examples/slurm_multi_node/slurm_multinode_sapo.sh b/skyrl-train/examples/slurm_multi_node/slurm_multinode_sapo.sh
new file mode 100644
index 000000000..050f6d1d6
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+++ b/skyrl-train/examples/slurm_multi_node/slurm_multinode_sapo.sh
@@ -0,0 +1,303 @@
+#!/bin/bash
+#SBATCH --job-name=sapo-4B
+#SBATCH --partition=main
+#SBATCH --nodes=2                # Number of nodes
+#SBATCH --ntasks-per-node=1      # One task per node
+#SBATCH --cpus-per-task=128      # cpu-cores per task (>1 if multi-threaded tasks)
+#SBATCH --gres=gpu:8
+#SBATCH --gpus-per-node=8
+#SBATCH --mem=0
+#SBATCH --exclusive
+#SBATCH --time=500:00:00
+#SBATCH --output=logs/sapo/4B/dapo_math/%x_%j.out
+#SBATCH --error=logs/sapo/4B/dapo_math/%x_%j.err
+
+# This script runs the training of RL on multi-nodes. It does resume automatically from latest checkpoint if the run crashes.
+# Example run with Qwen3-30B SAPO with new model engine
+
+set -x
+
+# Colocated DAPO training+generation for Qwen3-4B-Base on DAPO training data and validate on AIME 2024.
+# sbatch slurm_dapo_multinode.sh
+
+# Determine the script source directory
+unset VIRTUAL_ENV
+
+# Determine script source directory
+if [[ -n "${SLURM_SUBMIT_DIR:-}" ]]; then
+    cd "$SLURM_SUBMIT_DIR"
+    SCRIPT_SOURCE_DIR="$SLURM_SUBMIT_DIR"
+else
+    SCRIPT_SOURCE_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+fi
+
+REPO_ROOT=$(cd "$SCRIPT_SOURCE_DIR/../../.." && pwd)
+
+# The skyrl-train directory is where pyproject.toml lives.
+PROJECT_DIR="$REPO_ROOT/skyrl-train"
+
+# Ray + uv REQUIRE working_dir == directory containing pyproject.toml
+cd "$PROJECT_DIR"
+
+export PYTHONPATH="$PROJECT_DIR:$PYTHONPATH"
+
+PYTHON="$REPO_ROOT/skyrl/bin/python"
+
+echo "[INFO] REPO_ROOT=$REPO_ROOT"
+echo "[INFO] PYTHON=$PYTHON"
+echo "[INFO] PYTHONPATH=$PYTHONPATH"
+
+
+# can make training faster depending on clusters
+# this setting is optimized for H200 GPUs with NVLink and InfiniBand
+export NCCL_IBEXT_DISABLE=1
+export NCCL_NVLS_ENABLE=1
+export NCCL_IB_HCA=mlx5
+export UCX_NET_DEVICES=mlx5_0:1,mlx5_1:1,mlx5_2:1,mlx5_3:1,mlx5_4:1,mlx5_5:1,mlx5_6:1,mlx5_7:1
+
+# for triton
+export TRITON_ALLOW_HOPPER=1
+
+############################################
+
+# Determine how many nodes were allocated. 
+NNODES=${SLURM_JOB_NUM_NODES}
+export NNODES
+
+# Determine how many GPUs we actually have on the master node.
+# Carefull! Assumes all nodes have same number of GPUs! 
+# SLURM sets SLURM_GPUS_PER_NODE only when #SBATCH --gpus-per-node is used, not with --gres.
+# uncomment below line to manually set number of gpus per node if not using --gpus-per-node
+# export SLURM_GPUS_PER_NODE=8
+# SLURM_GPUS_PER_NODE=${SLURM_GPUS_PER_NODE:-$(nvidia-smi -L | wc -l)} # 8
+# export SLURM_GPUS_PER_NODE
+echo "SLURM_GPUS_PER_NODE: $SLURM_GPUS_PER_NODE"
+echo "SLURM_SUBMIT_DIR: $SLURM_SUBMIT_DIR"
+echo "REPO_ROOT: $REPO_ROOT"
+
+############################################
+# 1.          Experiment Params            #
+############################################
+
+MODEL_NAME="Qwen/Qwen3-4B-Base"
+DATA_DIR="$HOME/data/dapo"
+TRAIN_FILE="$DATA_DIR/dapo-math-17k-cleaned.parquet"
+TEST_FILE="$DATA_DIR/aime-2024-cleaned.parquet"
+
+# --- Resource Configuration ---
+NUM_NODES=$NNODES                   # Use the SLURM count defined earlier
+NUM_GPUS_PER_NODE=$SLURM_GPUS_PER_NODE
+NUM_INFERENCE_ENGINES=16
+INFERENCE_ENGINE_TENSOR_PARALLEL_SIZE=1
+
+# --- DAPO / Algorithm Parameters ---
+ADVANTAGE_ESTIMATOR="grpo"
+POLICY_LOSS_TYPE="sapo"
+LOSS_REDUCTION="sequence_mean" # must be sequence_mean for SAPO
+USE_KL_LOSS=false
+TAU_POS=1.0
+TAU_NEG=1.05
+CLIP_RATIO_C=10.0
+
+# Overlong punishment settings for DAPO
+APPLY_OVERLONG_FILTERING=false
+OVERLONG_BUFFER_LEN=$((1024 * 4))
+OVERLONG_BUFFER_PENALTY_FACTOR=1.0
+
+# --- Generator / Sampling Parameters ---
+TEMPERATURE=1.0
+TOP_P=1.0
+EVAL_TOP_P=0.7
+MAX_PROMPT_LENGTH=$((1024 * 2))
+MAX_RESPONSE_LENGTH=$((1024 * 8))
+GENERATOR_BACKEND="vllm"
+GPU_MEMORY_UTILIZATION=0.8
+RUN_ENGINES_LOCALLY=true
+WEIGHT_SYNC_BACKEND="nccl"
+ASYNC_ENGINE=false
+BATCHED=true
+ENV_CLASS="aime"
+
+# --- Training Hyperparameters ---
+TRAIN_BATCH_SIZE=256
+MINI_BATCH_SIZE=32
+MICRO_FORWARD_BATCH_SIZE=8
+MICRO_TRAIN_BATCH_SIZE=4
+EPOCHS=20
+LR=1e-6
+NUM_WARMUP_STEPS=160
+WEIGHT_DECAY=0.1
+MAX_GRAD_NORM=1.0
+STRATEGY="fsdp2"
+COLOCATE_ALL=true
+ENFORCE_EAGER=true # cuda graphs can cause some instability
+
+# --- Evaluation & Samples ---
+N_SAMPLES_PER_PROMPT=16
+EVAL_N_SAMPLES_PER_PROMPT=32
+EVAL_BATCH_SIZE=1024
+EVAL_BEFORE_TRAIN=false
+EVAL_INTERVAL=10
+UPDATE_EPOCHS_PER_BATCH=1
+
+# --- Logging & Checkpointing ---
+export WANDB_API_KEY=.... # set your WANDB_API_KEY before running the script if using wandb
+LOGGER="wandb"  # change to "console" to print to stdout
+PROJECT_NAME="RL-PRs"
+RUN_NAME="qwen3-4B-base-sapo-fsdp2-skyrl"
+EXPORT_PATH="$HOME/exports/dapo_qwen3_4b_base_skyrl"
+CKPT_PATH="$HOME/ckpts/dapo_qwen3_4b_base_skyrl"
+CKPT_INTERVAL=-1
+HF_SAVE_INTERVAL=-1
+RESUME_MODE="latest"
+MAX_CKPTS_TO_KEEP=3
+
+############################################
+# 2.           Start Ray cluster           #
+############################################
+
+export VLLM_ATTENTION_BACKEND=FLASH_ATTN
+export RAY_memory_monitor_refresh_ms=0
+export RAY_LOGGING_LEVEL=DEBUG
+export HYDRA_FULL_ERROR=1
+
+# Let Ray know how many nodes to expect
+export RAY_NUM_NODES=$NNODES
+
+############################################
+# 3.          Start Ray Head               #
+############################################
+
+# Get head node and its IP
+nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
+nodes_array=($nodes)
+head_node=${nodes_array[0]}
+head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address)
+
+# Convert to IPv4 if needed
+if [[ "$head_node_ip" == *" "* ]]; then
+  IFS=' ' read -ra ADDR <<<"$head_node_ip"
+  if [[ ${#ADDR[0]} -gt 16 ]]; then
+    head_node_ip=${ADDR[1]}
+  else
+    head_node_ip=${ADDR[0]}
+  fi
+  echo "IPV6 address detected. Using IPV4: $head_node_ip"
+fi
+
+port=6379
+ip_head=$head_node_ip:$port
+export MASTER_ADDR=$head_node_ip
+export MASTER_PORT=$port
+export ip_head
+
+echo "Starting Ray HEAD at $head_node ($ip_head)"
+until nvidia-smi > /dev/null 2>&1; do
+  echo "Waiting for GPU visibility..."
+  sleep 2
+done
+srun --nodes=1 --ntasks=1 -w "$head_node" \
+  uv run --python "$PYTHON" --active -- ray start --head --node-ip-address="$head_node_ip" \
+  --port=$port --num-cpus "$SLURM_CPUS_PER_TASK" --num-gpus "$SLURM_GPUS_PER_NODE" --block &
+
+sleep 10
+
+############################################
+# 4.          Start Ray Workers            #
+############################################
+
+worker_num=$((SLURM_JOB_NUM_NODES - 1))
+for ((i = 1; i <= worker_num; i++)); do
+  node_i=${nodes_array[$i]}
+  echo "Starting WORKER $i at $node_i"
+  until nvidia-smi > /dev/null 2>&1; do
+    echo "Waiting for GPU visibility..."
+    sleep 2
+  done
+  srun --nodes=1 --ntasks=1 -w "$node_i" \
+    uv run --python "$PYTHON" --active -- ray start --address "$ip_head" \
+    --num-cpus "$SLURM_CPUS_PER_TASK" --num-gpus "$SLURM_GPUS_PER_NODE" --block &
+  sleep 5
+done
+
+# Final launch barrier
+sleep 10
+
+############################################
+# 5.    Confirm Ray cluster resources      #
+############################################
+
+uv run --python "$PYTHON" --active -- python - << 'EOF'
+import ray
+ray.init(address="auto")
+print("Cluster resources:", ray.cluster_resources())
+EOF
+
+############################################
+# 6.    Launch training script via srun    #
+############################################
+
+echo "Using $SLURM_NNODES nodes for training..."
+
+srun --overlap --nodes=$NNODES --ntasks=1 -w "$head_node" \
+    uv run --python "$PYTHON" --active --extra vllm \
+    -m examples.algorithms.dapo.main_dapo \
+    data.train_data="['$TRAIN_FILE']" \
+    data.val_data="['$TEST_FILE']" \
+    trainer.algorithm.advantage_estimator="$ADVANTAGE_ESTIMATOR" \
+    trainer.algorithm.policy_loss_type="$POLICY_LOSS_TYPE" \
+    +trainer.algorithm.overlong_buffer.len=$OVERLONG_BUFFER_LEN \
+    +trainer.algorithm.overlong_buffer.penalty_factor=$OVERLONG_BUFFER_PENALTY_FACTOR \
+    trainer.algorithm.loss_reduction=$LOSS_REDUCTION \
+    generator.enforce_eager=$ENFORCE_EAGER \
+    generator.apply_overlong_filtering=$APPLY_OVERLONG_FILTERING \
+    generator.sampling_params.temperature=$TEMPERATURE \
+    generator.sampling_params.top_p=$TOP_P \
+    generator.eval_sampling_params.top_p=$EVAL_TOP_P \
+    generator.eval_sampling_params.temperature=$TEMPERATURE \
+    trainer.algorithm.use_kl_loss=$USE_KL_LOSS \
+    trainer.algorithm.clip_ratio_c=$CLIP_RATIO_C \
+    trainer.policy.model.path="$MODEL_NAME" \
+    trainer.placement.colocate_all=$COLOCATE_ALL \
+    trainer.strategy="$STRATEGY" \
+    trainer.placement.policy_num_nodes=$NUM_NODES \
+    trainer.placement.policy_num_gpus_per_node=$NUM_GPUS_PER_NODE \
+    trainer.policy.fsdp_config.fsdp_size=$NUM_GPUS_PER_NODE \
+    generator.num_inference_engines=$NUM_INFERENCE_ENGINES \
+    generator.inference_engine_tensor_parallel_size=$INFERENCE_ENGINE_TENSOR_PARALLEL_SIZE \
+    trainer.epochs=$EPOCHS \
+    trainer.algorithm.tau_pos=$TAU_POS \
+    trainer.algorithm.tau_neg=$TAU_NEG \
+    trainer.eval_batch_size=$EVAL_BATCH_SIZE \
+    trainer.eval_before_train=$EVAL_BEFORE_TRAIN \
+    trainer.eval_interval=$EVAL_INTERVAL \
+    trainer.update_epochs_per_batch=$UPDATE_EPOCHS_PER_BATCH \
+    trainer.train_batch_size=$TRAIN_BATCH_SIZE \
+    trainer.policy_mini_batch_size=$MINI_BATCH_SIZE \
+    trainer.micro_forward_batch_size_per_gpu=$MICRO_FORWARD_BATCH_SIZE \
+    trainer.micro_train_batch_size_per_gpu=$MICRO_TRAIN_BATCH_SIZE \
+    trainer.ckpt_interval=$CKPT_INTERVAL \
+    trainer.max_prompt_length=$MAX_PROMPT_LENGTH \
+    generator.sampling_params.max_generate_length=$MAX_RESPONSE_LENGTH \
+    trainer.policy.optimizer_config.lr=$LR \
+    trainer.policy.optimizer_config.num_warmup_steps=$NUM_WARMUP_STEPS \
+    trainer.policy.optimizer_config.weight_decay=$WEIGHT_DECAY \
+    trainer.policy.optimizer_config.max_grad_norm=$MAX_GRAD_NORM \
+    generator.backend="$GENERATOR_BACKEND" \
+    generator.run_engines_locally=$RUN_ENGINES_LOCALLY \
+    generator.weight_sync_backend="$WEIGHT_SYNC_BACKEND" \
+    generator.async_engine=$ASYNC_ENGINE \
+    generator.batched=$BATCHED \
+    environment.env_class="$ENV_CLASS" \
+    generator.n_samples_per_prompt=$N_SAMPLES_PER_PROMPT \
+    generator.eval_n_samples_per_prompt=$EVAL_N_SAMPLES_PER_PROMPT \
+    generator.gpu_memory_utilization=$GPU_MEMORY_UTILIZATION \
+    trainer.logger="$LOGGER" \
+    trainer.project_name="$PROJECT_NAME" \
+    trainer.run_name="$RUN_NAME" \
+    trainer.export_path="$EXPORT_PATH" \
+    trainer.hf_save_interval=$HF_SAVE_INTERVAL \
+    trainer.resume_mode="$RESUME_MODE" \
+    trainer.max_ckpts_to_keep=$MAX_CKPTS_TO_KEEP \
+    trainer.ckpt_path="$CKPT_PATH" \
+    $@
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