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How to run an existing verl config on one GPU with Mini Verl

Point miniVERL at a verl config tree, switch to GRPO and check the run with a dry run.

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

Who it is forA team with a verl experiment that wants to test it on one consumer GPU without rewriting the config.

The problemA verl config written for a cluster cannot be tried locally without changing its structure and overrides.

The outcomeA validated dry run of the verl config with GRPO and local GPU settings, ready for a real local run.

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 miniVERL with python -m pip install "miniverl[train,hydra]".
  2. Locate the verl trainer config directory on disk, for example /path/to/verl/trainer/config.
  3. Run miniverl run --verl-config-path /path/to/verl/trainer/config --verl-config-name ppo_trainer --dry-run algorithm.adv_estimator=grpo actor_rollout_ref.rollout.n=8 trainer.n_gpus_per_node=8 and read the output. (inferred, check the README)
  4. Read the direct-config guide to bind your data and reward function before a real run.

Prompt for your agent

Use the miniverl repo (DaoyuanLi2816/mini-verl) to dry-run a verl config at <verl-config-path> with the ppo_trainer config name and GRPO overrides. Report whether the dry run succeeds and list any overrides that failed. Verify that no training starts during the dry run.

Caveat. The README says the tool supports documented verl configuration profiles, so configs outside those profiles may not map cleanly; check the compatibility matrix first.

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