Run multi-host RL training for Qwen3-30b-a3b on TPU v6e

This tutorial shows you how to run multi-host reinforcement learning (RL) training on a Tensor Processing Unit (TPU) v6e-32 cluster by using MaxText and Cluster Toolkit. You use Cluster Toolkit to execute a multi-host training workload and export the results back to Hugging Face format for serving.

Workload summary

Running this RL post-training workload on Cloud TPU and GKE involves the following components:

  • Model: Qwen3 30B A3B Instruct (Qwen/Qwen3-30B-A3B-Instruct-2507), an advanced open-weights instruction-tuned model. In this tutorial, the model weights are downloaded from Hugging Face, converted into MaxText-compatible format, and deployed on a GKE cluster with Cloud TPU v6e infrastructure.
  • Reinforcement Learning (RL) algorithm: Group Relative Policy Optimization (GRPO) using MaxText and Pathways. The workload executes a scalable post-training pipeline using MaxText's built-in RL trainer (maxtext.trainers.post_train.rl.train_rl) paired with a vLLM sampler backend. GRPO optimizes the policy by generating a group of outputs per prompt and evaluating relative rewards without needing a separate critic model.
  • Dataset and template: OpenMathInstruct-2 (openmathinstruct2_rl.json), a large-scale math instruction-tuning dataset. The workload uses structured chat templates during the RL rollout phase to enhance multi-step mathematical reasoning capabilities.
  • Infrastructure and framework: GKE, managed using Cluster Toolkit, on Cloud TPU v6e (ct6e-standard-4t, with a 4x8 topology). The training framework is MaxText (tpu_post_training:0.2.4) with Pathways and JAX backend.

Objectives

  • Install Cluster Toolkit and its dependencies.
  • Deploy a Cluster Toolkit cluster.
  • Convert a Hugging Face model to MaxText format.
  • Run an RL training workload on the TPU v6e cluster.
  • Convert the fine-tuned model back to Hugging Face format for serving.

Costs

In this document, you use the following billable components of Google Cloud:

To generate a cost estimate based on your projected usage, use the pricing calculator.

New Google Cloud users might be eligible for a free trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

  • You need a Hugging Face access token to use this tutorial. You can sign up for a free account at Hugging Face. After you have an account, generate an access token:

    1. On the Welcome to Hugging Face page, click your account avatar and select Access tokens.
    2. On the Access tokens page, click Create new token.
    3. Select the Read token type and enter a name for your token.
    4. Your access token is displayed. Save the token in a safe place.

To get the permissions that you need to complete this tutorial, ask your administrator to grant you the following IAM roles on your project:

For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Set up your environment variables

Set up your environment variables:

export PROJECT="YOUR_PROJECT_ID"
export REGION="YOUR_REGION"
export ZONE="YOUR_ZONE"
export CLUSTER_NAME="YOUR_CLUSTER_NAME"
export GCS_BUCKET="YOUR_BUCKET_NAME"
export CLOUD_IMAGE_NAME="us-docker.pkg.dev/cloud-tpu-images/maxtext-images/tpu_post_training:0.2.4"
export COMPUTE_TYPE="ct6e-standard-4t"
export TOPOLOGY="4x8"
export CLUSTER_NODEPOOL_COUNT=1
export RESERVATION="YOUR_RESERVATION_NAME"
export MODEL_NAME="qwen3-30b-a3b"
export CLUSTER_TOOLKIT_VERSION="v1.103.0"
export HF_TOKEN="YOUR_HF_TOKEN"

Replace the following:

  • YOUR_PROJECT_ID: the ID of your Google Cloud project.
  • YOUR_REGION: the region where your TPU reservation is located and where you deploy your cluster.
  • YOUR_ZONE: the zone where your TPU reservation is located and where you deploy your cluster.
  • YOUR_CLUSTER_NAME: the name of your GKE cluster. The name must start with a lowercase letter, contain only lowercase letters, numbers, and hyphens, and be 20 characters or fewer.
  • YOUR_BUCKET_NAME: a globally unique name for a Cloud Storage bucket.
  • YOUR_RESERVATION_NAME: the name of your TPU v6e reservation.
  • YOUR_HF_TOKEN: your Hugging Face access token.

Install Cluster Toolkit dependencies

To complete this tutorial from a Linux or macOS client or workstation, install the following dependencies:

If you're using Cloud Shell, then you can skip this section.

Install Cluster Toolkit

Download and extract the prebuilt bundle for the specific version of Cluster Toolkit defined in CLUSTER_TOOLKIT_VERSION (v1.103.0) into your current working directory:

wget -qO- "https://github.com/GoogleCloudPlatform/cluster-toolkit/releases/download/${CLUSTER_TOOLKIT_VERSION:?}/gcluster_bundle_linux_amd64.tgz" | tar -xz

Authenticate your environment

Before running the deployment scripts, you must authenticate the Google Cloud CLI and set your Application Default Credentials (ADC). This ensures that Cluster Toolkit can provision resources and push images to Artifact Registry on your behalf:

gcloud auth login
gcloud auth application-default login
gcloud auth application-default set-quota-project "${PROJECT:?}"

gcloud auth configure-docker gcr.io --quiet
gcloud auth configure-docker "${REGION:?}-docker.pkg.dev" --quiet

Create your Cluster Toolkit cluster

To create and deploy a Cluster Toolkit cluster with 32 v6e TPU chips, complete the following steps:

  1. Create a Cloud Storage bucket:

    gcloud storage buckets create "gs://${GCS_BUCKET:?}" --project="${PROJECT:?}" --location="${REGION:?}" || true

  2. Copy the Cluster Toolkit blueprint to your current working directory:

    cp examples/gke-tpu-v6e/gke-tpu-v6e-advanced.yaml .

  3. By default, your cluster node pool service account doesn't have the required permissions to write checkpoints to your Cloud Storage bucket. To allow the node pool service account (gke-np-sa) to write to your Cloud Storage bucket, and to avoid regional machine-type stockouts, edit the file gke-tpu-v6e-advanced.yaml to grant your node pool service account the Storage Admin role (roles/storage.admin), switch the system node pool machine type to e2-standard-8, and pin the system node pool to your target zone:

    sed -i "s/- storage.objectViewer/- storage.admin/" gke-tpu-v6e-advanced.yaml
sed -i "s/n2-standard-8/e2-standard-8/" gke-tpu-v6e-advanced.yaml
sed -i '/system_node_pool_machine_type/a \      system_node_pool_zones: [$(vars.zone)]' gke-tpu-v6e-advanced.yaml

  4. Use the gcluster deploy command to deploy your Cluster Toolkit cluster by using the blueprint gke-tpu-v6e-advanced.yaml and passing the required variables by using the --vars flag:

    ./gcluster deploy gke-tpu-v6e-advanced.yaml \
    --vars project_id="${PROJECT:?}" \
    --vars deployment_name="${CLUSTER_NAME:?}" \
    --vars region="${REGION:?}" \
    --vars zone="${ZONE:?}" \
    --vars num_slices="${CLUSTER_NODEPOOL_COUNT:?}" \
    --vars tpu_topology="${TOPOLOGY:?}" \
    --vars authorized_cidr="0.0.0.0/0" \
    --vars reservation="${RESERVATION:-}" \
    -l IGNORE --auto-approve -w

Convert the model to MaxText format

To train the model in MaxText format, you must convert it from Hugging Face format to MaxText format.

  1. To simplify subsequent commands, use the gcluster job config command to configure your default project, cluster, and location:

    # Configure gcluster Defaults
./gcluster job config set project "${PROJECT:?}"
./gcluster job config set cluster "${CLUSTER_NAME:?}"
./gcluster job config set location "${REGION:?}"

  2. Use the gcluster job submit command to convert the model from Hugging Face format to MaxText format and store it in your Cloud Storage bucket:

    ./gcluster job submit \
  --name qwen-hf-to-mt \
  --num-slices 1 \
  --image "${CLOUD_IMAGE_NAME:?}" \
  --compute-type "${COMPUTE_TYPE:?}" \
  --topology "${TOPOLOGY:?}" \
  --await-job-completion \
  --command "[ \"\$JOB_COMPLETION_INDEX\" != \"0\" ] || \
  python3 -m maxtext.checkpoint_conversion.to_maxtext \
  model_name=${MODEL_NAME:?} \
  hf_access_token=${HF_TOKEN:?} \
  --hf_model_path='Qwen/Qwen3-30B-A3B-Instruct-2507' \
  base_output_directory=gs://${GCS_BUCKET:?}/${MODEL_NAME}/max-text-format/ \
  scan_layers=True \
  use_multimodal=False \
  skip_jax_distributed_system=true \
  checkpoint_storage_use_zarr3=0 \
  checkpoint_storage_use_ocdbt=0 \
  hardware=cpu \
  --lazy_load_tensors=True"

  3. Optional: To stream the conversion logs while the job is running, open a separate terminal in the working directory where you extracted gcluster, and run the gcluster job logs command:

    # Use the list command to check status
./gcluster job list

# Check progress of the job (--main-only targets the coordinator pod (Job Index 0, Pod Index 0) to avoid duplicate logs from other workers)
./gcluster job logs qwen-hf-to-mt --main-only -f

  4. In your original terminal, verify that the converted model files are available in your Cloud Storage bucket:

    gcloud storage ls "gs://${GCS_BUCKET:?}/${MODEL_NAME:?}/max-text-format/"

Start the training workload

After the conversion process has completed, start the RL training workload:

./gcluster job submit \
  --name="qwen-rl" \
  --num-slices=1 \
  --image="${CLOUD_IMAGE_NAME:?}" \
  --compute-type="${COMPUTE_TYPE:?}" \
  --topology="${TOPOLOGY:?}" \
  --pathways \
  --pathways-gcs-location="gs://${GCS_BUCKET:?}/pathways/" \
  --gke-ttl-after-finished="24h" \
  --restarts=0 \
  --env="GRPC_DNS_RESOLVER=native" \
  --env="FLAGS_pathways_enforce_subset_devices_form_subslice=false" \
  --pathways-proxy-env="GRPC_DNS_RESOLVER=native" \
  --pathways-proxy-env="FLAGS_pathways_enforce_subset_devices_form_subslice=false" \
  --pathways-server-env="GRPC_DNS_RESOLVER=native" \
  --pathways-server-env="FLAGS_pathways_enforce_subset_devices_form_subslice=false" \
  --pathways-worker-env="GRPC_DNS_RESOLVER=native" \
  --pathways-worker-env="FLAGS_pathways_enforce_subset_devices_form_subslice=false" \
  --command="(echo 190G > /sys/fs/cgroup/memory.max || echo 190G > /sys/fs/cgroup/memory/memory.limit_in_bytes) 2>/dev/null || true && \
    export VLLM_HOST_IP=\$(hostname -I | awk '{print \$1}') && \
    export VLLM_ENABLE_V1_MULTIPROCESSING=0 && \
    python3 -c \"import pathlib, tunix.generate.vllm_sampler as vs; p = pathlib.Path(vs.__file__); p.write_text(p.read_text().replace('reshard_chunk_size: Optional[int] = None', 'reshard_chunk_size: Optional[int] = 4').replace('reshard_chunk_size=self.config.reshard_chunk_size', 'reshard_chunk_size=4'))\" && \
    python3 -c \"import pathlib, re; p = pathlib.Path('/deps/src/maxtext/trainers/post_train/rl/utils_rl.py'); p.write_text(re.sub('optax[.]adamw[(][^)]+[)]', 'optax.adafactor(learning_rate=learning_rate)', p.read_text()))\" && \
    JAX_PLATFORMS=proxy,cpu ENABLE_PATHWAYS_PERSISTENCE=1 \
    python3 -m maxtext.trainers.post_train.rl.train_rl \
    run_name=rl \
    base_output_directory=gs://${GCS_BUCKET}/${MODEL_NAME:?}/trained/ \
    model_name=${MODEL_NAME} \
    load_parameters_path=gs://${GCS_BUCKET}/${MODEL_NAME}/max-text-format/0/items/ \
    hf_access_token=${HF_TOKEN:?} \
    data_template_path=maxtext/examples/chat_templates/openmathinstruct2_rl.json \
    num_batches=50 \
    num_test_batches=0 \
    batch_size=8 \
    train_micro_batch_size=8 \
    max_target_length=512 \
    max_prefill_predict_length=256 \
    mu_dtype=bfloat16 \
    grad_dtype=bfloat16 \
    rollout_tensor_parallelism=1 \
    rollout_expert_parallelism=4 \
    trainer_devices_fraction=0.5 \
    sampler_devices_fraction=0.5 \
    tokenizer_path='Qwen/Qwen3-30B-A3B-Instruct-2507' \
    ici_tensor_parallelism=2 \
    ici_expert_parallelism=4 \
    ici_fsdp_parallelism=-1 \
    hbm_utilization_vllm=0.55 \
    remat_policy=full \
    async_scheduling=False \
    allow_split_physical_axes=true \
    ragged_gather_reduce_fallback=True \
    enable_dp_attention=False \
    decode_sampling_temperature=0.8 \
    decode_sampling_top_k=50 \
    decode_sampling_nucleus_p=0.95 \
    learning_rate=2e-5 \
    learning_rate_schedule_steps=100 \
    rl.num_generations=4 \
    rl.reshard_chunk_size=4 \
    debug=True \
    vllm_hf_overrides='{\"architectures\": [\"MaxTextForCausalLM\"]}' \
    vllm_additional_config=\"{'maxtext_config': {'model_name': '${MODEL_NAME}', 'model_call_mode': 'inference', 'enable_dp_attention': false, 'allow_split_physical_axes': true, 'use_ragged_sort': false, 'ragged_gather_reduce_fallback': true, 'prefuse_moe_weights': true, 'weight_dtype': 'bfloat16'}}\""

  1. Check the status, wait for the coordinator Pod to become ready, and stream the logs of the training job:

    # Use the list command to check status (initially shows "Suspended" briefly while the CPU node pool scales up)
./gcluster job list

# Ensure kubectl credentials are configured
gcloud container clusters get-credentials "${CLUSTER_NAME:?}" \
    --location="${REGION:?}" \
    --project="${PROJECT:?}"

# Wait for the Pathways head pod to be ready before streaming logs
kubectl wait --for=condition=Ready pod \
    -l jobset.sigs.k8s.io/replicatedjob-name=pathways-head \
    --timeout=600s

# Stream training logs (runs for 50 steps; wait for "Post RL Training: ... mean_reward=..." and job status Succeeded before converting to Hugging Face)
kubectl logs -f \
    -l jobset.sigs.k8s.io/replicatedjob-name=pathways-head \
    -c workload-container

    Immediately after submission, the output from the ./gcluster job list command might briefly show Suspended while GKE scales up the Pathways coordinator CPU node pool (cpu-np). When the job begins, it outputs an initial pre-training evaluation at step 0 (Pre RL Training: ... mean_reward=...), pauses log output for several minutes during initial XLA compilation, and then trains for 50 steps, printing Reshard finished and rollout generations at each step without step numbers.

    Before you proceed to the next step, wait until the logs output Post RL Training: ... mean_reward=... and the output from the ./gcluster job list command shows the status of qwen-rl as Succeeded.

  2. Verify that the training checkpoints are generated in your Cloud Storage bucket:

    gcloud storage ls "gs://${GCS_BUCKET:?}/${MODEL_NAME:?}/trained/rl/checkpoints/actor/"

Convert the trained model back into Hugging Face format

  1. After the training workload has completed, convert the model back to Hugging Face format:

    ./gcluster job submit \
  --name="qwen-mt-to-hf" \
  --num-slices=1 \
  --image="${CLOUD_IMAGE_NAME:?}" \
  --compute-type="${COMPUTE_TYPE:?}" \
  --topology="${TOPOLOGY:?}" \
  --await-job-completion \
  --command="[ \"\$JOB_COMPLETION_INDEX\" != \"0\" ] || \
  python3 -m maxtext.checkpoint_conversion.to_huggingface \
  model_name=${MODEL_NAME:?} \
  hf_access_token=${HF_TOKEN:?} \
  load_parameters_path=gs://${GCS_BUCKET:?}/${MODEL_NAME}/trained/rl/checkpoints/actor/50/model_params/ \
  base_output_directory=gs://${GCS_BUCKET}/${MODEL_NAME}/hf-trained/ \
  skip_jax_distributed_system=true \
  hardware=cpu \
  scan_layers=True \
  use_multimodal=False \
  weight_dtype=bfloat16 \
  --override_model_architecture"

  2. Optional: To monitor the live conversion logs while the preceding --await-job-completion command waits in your first terminal, open a separate terminal in the working directory where you extracted gcluster, and use the gcluster job logs command:

    # Use the list command to check status
./gcluster job list

# Check progress of the job (--main-only targets the coordinator pod (Job Index 0, Pod Index 0) to avoid duplicate logs from other workers)
./gcluster job logs qwen-mt-to-hf --main-only -f
# The trained model is now available in gs://${GCS_BUCKET}/${MODEL_NAME}/hf-trained/

  3. In your original terminal, verify that the trained Hugging Face model weights and configuration files are present in your Cloud Storage bucket:

    gcloud storage ls -l --readable-sizes "gs://${GCS_BUCKET:?}/${MODEL_NAME:?}/hf-trained/"

Clean up

To avoid incurring additional charges, use the gcluster destroy command to delete the resources created during this tutorial:

./gcluster destroy "${CLUSTER_NAME:?}" --auto-approve
gcloud storage rm -r "gs://${GCS_BUCKET:?}"

# To delete the local deployment folder and copied blueprint
rm -rf .ghpc "${CLUSTER_NAME:?}" gke-tpu-v6e-advanced.yaml

What's next