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Reinforcement Learning on single-host TPUs

This tutorial demonstrates step-by-step instructions for setting up the environment and then training the Llama3.1 8B-IT model on the GSM8K math reasoning dataset using a single host TPU-VM such as v6e-8/v5p-8.

We utilize two RL algorithms, implemented via the Tunix library, to enhance the model's reasoning capabilities:

  • Group Relative Policy Optimization (GRPO): GRPO is an RL algorithm designed to enhance the reasoning abilities of LLMs. It is a variant of Proximal Policy Optimization (PPO) that reduces memory usage by eliminating the need for a separate value function model. GRPO works by generating multiple responses for a given prompt, evaluating these responses using a reward model, and then calculating a relative advantage based on the group's performance to update the policy.

  • Group Sequence Policy Optimization (GSPO): GSPO is an RL algorithm that improves training efficiency and performance of LLMs by using sequence-level importance ratios and operations. GSPO defines the importance ratio based on sequence likelihood and performs sequence-level clipping, rewarding, and optimization.

For efficient model inference and response generation during this process, we rely on the vLLM library.

Let's get started!

Install MaxText and post-training dependencies

For instructions on installing MaxText with post-training dependencies on your VM, please refer to the official documentation and use the maxtext[tpu-post-train] installation path to include all necessary post-training dependencies.

Note: If you have previously installed MaxText with a different option (e.g., maxtext[tpu]), we strongly recommend using a fresh virtual environment for maxtext[tpu-post-train] to avoid potential library version conflicts.

Setup environment variables

Login to Hugging Face. Provide your access token when prompted: You can generate one at https://huggingface.co/settings/tokens.

hf auth login

Set up the following environment variables to configure your training run. Replace placeholders with your actual values.

# -- Model configuration --
# The MaxText model name. See `src/maxtext/configs/types.py` for `ModelName` for a
# full list of supported models.
export MODEL=<MODEL_NAME> # e.g. 'llama3.1-8b-Instruct'

# -- MaxText configuration --
# Use a GCS bucket you own to store logs and checkpoints.
# You can list your buckets and their locations in the
# [Cloud Console](https://console.cloud.google.com/storage/browser) or via
# `gcloud storage buckets list --format="table(name, location)"`.
export BASE_OUTPUT_DIRECTORY=<GCS_BUCKET> # e.g., gs://my-bucket/maxtext-runs

# An arbitrary string to identify this specific run.
# We recommend to include the model, user, and timestamp.
# Note: Kubernetes requires workload names to be valid DNS labels (lowercase, no underscores or periods).
export RUN_NAME=<RUN_NAME>

# Number of accelerator chips per VM.
# - TPU v5e (single host): 8
# - TPU v5p (single host): 4
# - TPU v6e (single host): 8
export CHIPS_PER_VM=<CHIPS_PER_VM>

Get your model checkpoint

Option 1: Using an existing MaxText checkpoint

If you already have a MaxText-compatible model checkpoint, simply set the following environment variable and move on to the next section.

export MAXTEXT_CKPT_PATH=<CKPT_PATH> # e.g., gs://my-bucket/my-model-checkpoint/0/items

Option 2: Converting from a Hugging Face checkpoint

Refer the steps in Hugging Face to MaxText to convert a hugging face checkpoint to MaxText. Make sure you have correct checkpoint files converted and saved. Similar to Option 1, set the following environment variable and move on to the next section.

export MAXTEXT_CKPT_PATH=<CKPT_PATH> # e.g., gs://my-bucket/my-model-checkpoint/0/items

Run GRPO

Run the following command for GRPO:

python3 -m maxtext.trainers.post_train.rl.train_rl \
  model_name=${MODEL?} \
  load_parameters_path=${MAXTEXT_CKPT_PATH?} \
  run_name=${RUN_NAME?} \
  base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
  chips_per_vm=${CHIPS_PER_VM?}

The overview of what this run will do is as follows:

  1. We load a policy model and a reference model. Both are copies of the model checkpoint you specified (e.g., Llama3.1-8b-Instruct).
  2. Evaluate the policy model's performance on GSM8K math reasoning benchmark.
  3. Train the policy model using GRPO.
  4. Evaluate the policy model's performance on GSM8K math reasoning benchmark after the post-training with GRPO.

By default, the above command will train the model using GRPOLearner from Tunix. To enable asynchronous RL training with AgenticGRPOLearner, we can set rl.use_agentic_rollout to true. An example command will be:

python3 -m maxtext.trainers.post_train.rl.train_rl \
  model_name=${MODEL?} \
  load_parameters_path=${MAXTEXT_CKPT_PATH?} \
  run_name=${RUN_NAME?} \
  base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
  chips_per_vm=${CHIPS_PER_VM?} \
  rl.use_agentic_rollout=True

Run GSPO

Run the following command for GSPO:

python3 -m maxtext.trainers.post_train.rl.train_rl \
  model_name=${MODEL?} \
  load_parameters_path=${MAXTEXT_CKPT_PATH?} \
  run_name=${RUN_NAME?} \
  base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
  loss_algo=gspo-token \
  chips_per_vm=${CHIPS_PER_VM?}

The overview of what this run will do is as follows:

  1. We load a policy model and a reference model. Both are copies of the model checkpoint you specified (e.g., Llama3.1-8b-Instruct).
  2. Evaluate the policy model's performance on GSM8K math reasoning benchmark.
  3. Train the policy model using GSPO.
  4. Evaluate the policy model's performance on GSM8K math reasoning benchmark after the post-training with GSPO.