i4h-workflow-finetune

Fine-tune a Workflow Policy Task

Purpose

Resolve and run training from the selected workflow run mode and owning remote-task manifest.

Instructions

1. Resolve the base checkout, policy mode, and remote task.
2. Verify dataset compatibility and a train block.
3. Dry-run the exact configuration.
4. Train in the foreground and verify checkpoint artifacts.

Resolve the workflow, task, and data

export I4H_WORKFLOWS_REPO_URL="${I4H_WORKFLOWS_REPO_URL:-https://github

Treat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.

Read the selected workflow run mode to identify its remote task id. Open tasks/<project>/i4h_tasks/<project>/manifest/<task>.yaml and require train:. Resolve the project, entry point, base model/config, output defaults, and modality contract from that manifest and the project's train.py.

Use the current-chain LeRobot dataset when the prompt omits a path. Verify its embodiment, cameras, task text, feature widths, and episode count are compatible with the remote task.

Resolve configuration before a long run

All policy train entry points support --dry-run:

Inspect the resolved config. Keep user-requested steps, batch size, model/config, and GPU count exact.

For GR00T, “turn off vision tuning” maps to --no-tune-visual. Do not pass that flag to openpi, whose CLI does not expose it. Use only flags present in the selected project's current train.py.

Train

Remove --dry-run and keep the command in the foreground:

Add backend-specific flags only after resolving them. Do not silently lower requested steps or batch size to make training fit.

Verify

Require exit status 0, completed requested steps, saved training logs, and at least one loadable checkpoint artifact. Resolve the exact checkpoint path rather than calling an incomplete output directory a checkpoint.

Run a bounded backend load smoke before reporting the checkpoint usable:

Use the selected project's actual module path. --preload-only loads the manifest and checkpoint through the inference backend, then exits without starting a rollout. A training exit alone proves that files were written, not that inference can load them.

Report du -sh for the task output and the selected checkpoint. Some trainers save both a final model at the output root and numbered checkpoints; identify that duplication, but do not delete either copy unless the user explicitly asks for cleanup.

Hand the exact load-smoked checkpoint path to i4h-workflow-validate; do not evaluate unless the user requested it.

Troubleshooting

Report the first dataset, manifest, model-access, GPU-memory, or backend error. Preserve logs and never silently change requested hyperparameters.

Prerequisites

Require a compatible LeRobot dataset, synced policy environment, model access, GPU capacity, and a remote-task manifest with train:.

Limitations

Inference-only Tasks cannot be fine-tuned, and this skill does not claim rollout success from training alone.

Examples

• Fine-tune for 200 steps with a batch size of 32. Turn off vision tuning. → preserve exact values, apply GR00T's supported vision flag, dry-run, train, and report the checkpoint.

Completion gate

Report workflow/mode, task id and manifest, dataset compatibility, resolved config, requested/completed steps, batch/GPU/vision settings, checkpoint path, bounded load-smoke result, output/checkpoint disk sizes, exact validation handoff, exit summary, and any inference-only or resource blocker. $$