Agent skill · nvidia
i4h-workflow-train-rl
Use when training, evaluating, or exporting Workflow policies with online RSL-RL or RLinf, including RL checkpoint and Workflow handoff.
What it needs
About 6k tokens when loaded.
What this skill does
Train a Workflow Policy with RL Purpose Resolve a maintained online-RL profile, verify its Scene/objective/model contracts, run the selected vectorized trainer, evaluate and export its artifact, and hand that artifact to normal Workflow policy validation. Requirements Run the Workflow setup skill first so its uv environments and pinned third-party checkouts are available. Use a CUDA-capable Isaac Lab/Arena runtime for the RSL-RL workflow. For Trocar, provide a local GR00T N1.5 3B base or SFT checkpoint and two visible local GPUs for the isolated controller and simulator runtimes. A compatible checkpoint is a complete local Hugging Face directory that the pinned GR00T N1.5/RLinf loader accepts without conversion; it must retain the N1.5 3B architecture and support the maintained three-camera plus 28-joint observation mapping and 28-D policy action head. Reject another model family, an exported inference-only Task artifact, or a checkpoint whose config changes those interfaces. Instructions 1. Resolve the checkout and supported profiles. 2. Confirm the Workflow, Scene, observations, actions, rewards, resets, termination, trainer, and runtime Task contracts. 3. Dry-run the exact requested configuration. 4. Preflight the selected trainer runtime and train in the foreground. 5. Evaluate simulator success, export the policy, and validate it through the normal Workflow runner. Resolve the checkout Treat this resolver as part of the skill contract. I4HWORKFLOWSREPOURL selects the clone source; I4HWORKFLOWS selects or reuses a checkout. Never replace an existing checkout. Resolve the profile and contracts Read the profile under ./rl/profiles/, its referenced declarative trainer config under ./rl/config/, Workflow, Scene manifest and implementation, Arena objective config, embodiment, and runtime policy Task manifest before a long run. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/i4h-workflow-train-rl