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How to resume an interrupted run and export its outputs with Mini Verl

Pick up a stopped training run from its checkpoint and export the adapter and configs for hand-off.

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

Who it is forA user whose local training run was interrupted and who needs to finish it and share the results with a larger setup.

The problemAn interrupted single-GPU run can lose progress, and moving the result to a bigger setup needs clean exports.

The outcomeA finished run with restored model, optimizer, data position and random state, plus exported adapters, datasets and a setup report.

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. Find the run ID used for the interrupted run in the output directory.
  2. Read the resume and export steps in docs/for-verl-users.md.
  3. Resume the run from its latest checkpoint following the steps in that guide.
  4. Export the adapter, datasets and configuration files with the steps in docs/verl-opd-scaleout.md.
  5. Read the report of remaining setup in the export to see what a larger environment still needs.

Prompt for your agent

In the miniverl repo (DaoyuanLi2816/mini-verl), resume the run with ID <run-id> from its latest checkpoint and export its adapter and configs to <export-dir>. Report the export paths and the remaining setup listed in the hand-off report. Verify that the resumed run continues from the saved step.

Caveat. The README exact resume claim is shown for the OPD reference workload, so verify recovery on your own run before relying on it.

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