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How to run a first PPO experiment on one GPU with Mini Verl

Install miniVERL, sample prompts and train a Qwen3-0.6B PPO run with a length reward.

  • Top pick
  • Research
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Who it is forAn ML engineer with one NVIDIA GPU who wants a working reinforcement learning run before adapting it to their own task.

The problemSetting up verl-style reinforcement learning usually assumes a multi-GPU cluster, so a single-GPU user has no simple starting point.

The outcomeA local PPO run that writes reward logs to rewards.jsonl and saves a resumable run under the chosen run ID.

What you need first

  • A single NVIDIA GPU with a CUDA-enabled PyTorch build installed from the PyTorch installer
  • Python with pip available
  • Enough GPU memory for the chosen model (see the single-GPU guide and hardware planning docs)

Steps

  1. Install the matching CUDA-enabled PyTorch build from the PyTorch installer, then run python -m pip install "miniverl[train,hydra]".
  2. Create a small prompt set with miniverl data sample --reward-profile target-length --rows 8 --out data/rl-prompts.parquet.
  3. Check the configuration without training using miniverl run --example hydra-ppo --bind reward.provider=target_length --dry-run.
  4. Start the run with miniverl run --example hydra-ppo --bind reward.provider=target_length --run-id local-ppo.
  5. Open rewards.jsonl in the run output to read the reward for each rollout.

Prompt for your agent

Work in the miniverl repo (DaoyuanLi2816/mini-verl) on this machine. Install it, sample 8 prompts into data/rl-prompts.parquet, run the hydra-ppo example with run ID <run-id>, and report the path to rewards.jsonl. Verify that the dry run passes before the full run starts.

Caveat. The README gives the workflow for a Qwen3-0.6B example on one NVIDIA GPU, and the CUDA PyTorch build must be chosen separately from the miniverl install.

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