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name: training description: "Builds VLA-Adapter fine-tuning plans and safe torchrun commands for LIBERO, CALVIN-style, and ALOHA data." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training (external-checkout adapter)

This sub-skill renders a training plan for an external native checkout. The generated skill contains only documentation and a command builder; it does not contain vla-scripts/finetune.py, does not run torchrun, and is not a self-contained training environment.

Before any native command, use an absolute checkout root:

export VLA_ADAPTER_REPO_ROOT=/abs/path/to/VLA-Adapter
cd "$VLA_ADAPTER_REPO_ROOT"
python -m pip install -e "$VLA_ADAPTER_REPO_ROOT"

The native source entrypoint is vla-scripts/finetune.py; the bundled build_finetune_command.py only prints a command beginning with cd <absolute-repo-root> && and never launches it. External prerequisites are the native checkout, its base dependencies, CUDA-capable PyTorch and suitable GPU memory, a compatible pretrained Prismatic VLM/config, the selected LIBERO/CALVIN/ALOHA dataset and TFDS/dlimp or robot stack, and W&B credentials or offline logging configuration as applicable.

Read and run

• Read references/training-workflows.md for end-to-end fine-tuning flows, LIBERO/CALVIN/ALOHA recipes, VRAM profiles, checkpoint output behavior, resume, and LoRA merge notes.
• Read references/configuration.md for important FinetuneConfig fields and option interactions.
• Read references/troubleshooting.md for CUDA OOM, FlashAttention, W&B, LoRA merge, dataset, and resume failures.
• Run python "$VLA_ADAPTER_SKILL_ROOT/sub-skills/training/scripts/build_finetune_command.py" --repo-root "$VLA_ADAPTER_REPO_ROOT" --help to inspect the renderer. It is not the training API and never launches torchrun.

Workflow

1. Route setup and data-root questions to setup-and-data first.
2. Pick the benchmark family and GPU profile.
3. Decide whether the run should use Pro (recommended), LoRA, frozen/full-save mode, proprioception, image augmentation, and checkpoint merging during training.
4. Generate a command with the bundled builder using an explicit --repo-root "$VLA_ADAPTER_REPO_ROOT"; inspect paths, W&B settings, GPU count, output directory, and storage impact.
5. Launch native vla-scripts/finetune.py only from the emitted checkout-root guard after confirming data, checkpoint/VLM paths, CUDA memory, and runtime.
6. After training, route checkpoint validation or offline LoRA merge to package-apis, and benchmark rollouts to evaluation.

Defaults to remember

• Pro version is recommended for most new runs.
• LIBERO/CALVIN recipes use 2 images, proprioception, LoRA rank 64, and 8-action chunks.
• ALOHA recipes use 3 images, 14D proprio/actions, 25-action chunks, and a 4-GPU launcher by default.
• W&B can run offline or be replaced with local logs for machines without credentials.

Do not

• Do not launch full training from this skill without confirming dataset size, checkpoint paths, output storage, GPU memory, and runtime budget.
• Do not treat CPU import success as proof that training can run.
• Do not patch ALOHA local-model source files without an explicit restore plan.