git:20260802.5fa1771 to git:20260813.941e608

3 added, 1 removed. Audit A to A.

---

name: train-policy

description: Use when planning, reviewing, or operating robot policy training across LeRobot, Isaac Lab, SONIC, and workflow YAMLs.

---

# Train Policy

## When To Use

Use this skill when a task asks how to train, fine-tune, evaluate, or export a

robot policy through NPA workbench tools. It is the workflow-level entry point

before choosing LeRobot, Isaac Lab, SONIC, or GR00T-specific skills.

## Procedure

1. Identify the policy family and data contract:

LeRobotDataset for LeRobot, Isaac Lab task config for RL, retargeted motion

artifacts for SONIC, or model-specific inputs for GR00T.

2. Select the GPU target with `skills/atomic/gpu-selection/SKILL.md`.

3. Configure input and output S3 prefixes. Checkpoints and evaluation artifacts

must be run-scoped.

4. Choose the executable path:

direct CLI for a single tool, SDK for application code, or SkyPilot YAML for

composed training workflows.

5. Verify command help and YAML parsing locally before live GPU submission.

## Three-Tier Contract

- CLI: `npa workbench lerobot train`, `npa workbench isaac-lab train`,

`npa workbench sonic train`, and related `eval`, `export`, `serve`, or

`infer` commands.

- SDK: use the workbench SDK modules for application code and shared helper

functions for request construction.

- YAML: `isaac-lab-rl-train.yaml`, `sonic-train-standalone.yaml`, and sim-to-real

workflow YAMLs are executable references. The parallel sweep is now the

`npa.workflow` spec `npa-workflows/isaac-lab-rl-sweep.yaml` (`--runtime`); its

- raw template is retired.

+ raw template is retired. GR00T N1.7 training uses the real

+ `npa-workflows/groot-1-7-finetune.yaml` toolRef path, with `gpu_count`

+ propagated into both H100 resources and the upstream trainer world size.

## Gotchas

- Do not route RT-core-dependent training or render validation to H100/H200.

- Do not substitute repository-local output directories for S3 artifact paths in

public examples.

- Treat tiny smoke trainers as verification substitutes only when the prompt

explicitly allows minimal production-input substitution.

- Keep W&B, Hugging Face, NGC, and S3 credentials redacted.

## Verify

```bash

npa/.venv/bin/python -m pytest npa/tests/guardrails/test_skills_index.py -q

```

The smoke test invokes training command help and parses the referenced training

YAMLs.