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name: rlinf description: "Routes RLinf repository tasks for distributed reinforcement learning setup, embodied and agentic workflows, extension development, evaluation operations, and debugging." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

RLinf repo skill

Use this skill when a task asks about RLinf, a distributed reinforcement-learning stack for embodied agents, reasoning/agentic LLM workflows, SFT/offline RL, reward models, Ray/Hydra cluster execution, or RLinf-specific source extensions.

This skill is operational guidance. It is not permission to run long training, evaluation, Ray cluster lifecycle commands, Docker builds, model/data downloads, robot motion, checkpoint conversion, or dataset mutation. Start with read-only inspection and ask for explicit approval before side-effecting or expensive work.

Fast start

1. Read references/package-overview.md for the architecture map and verified task/model/env routing constants.
2. Run scripts/rlinf_public_api_snapshot.py in the target environment when you need to verify the installed package version, imports, task types, model types, or env types.
3. Pick one sub-skill from the route table below. Most real tasks need exactly one primary sub-skill plus a setup or operations cross-link.
4. Read references/troubleshooting.md when symptoms span install, Ray, config, optional backends, runtime logs, or ambiguous ownership.
5. Check references/repo-provenance.md before trusting this skill for a newer checkout; refresh when the commit, dirty state, package metadata, examples, docs, or public APIs changed.

Sub-skill route table

User task signals	Read this sub-skill	What it covers
install target, Docker vs UV, Ray start/status, RLINF_NODE_RANK, multi-node, cluster.num_nodes, component_placement, heterogeneous node groups, worker/channel/placement APIs	setup-and-cluster	Installation planning, non-mutating environment probes, Ray cluster setup, Hydra cluster config, execution modes, scheduler mental model, and placement troubleshooting.
ManiSkill, LIBERO, RoboTwin, RoboCasa, Behavior, IsaacLab, Franka/real-world, OpenVLA/OFT/OpenPI/GR00T/DreamZero/StarVLA/Evo1, embodied PPO/GRPO/SAC/DAgger/RLT, reward data, offline RL, ROBOT_PLATFORM, MuJoCo/EGL	embodied-workflows	Embodied recipes, config preflight, simulator/model/asset requirements, real robot safety, reward data/offline/SFT intersections, and embodied troubleshooting.
math/VQA reasoning RL, Qwen/DeepSeek, GRPO/PPO with LLMs, SGLang/vLLM rollout, coding online RL, Continue integration, AgentLightning, SearchR1, rStar2, WideSeek-R1, SFT/VLM SFT, reward services	reasoning-agent-workflows	Reasoning/agentic recipes, rollout backend and data prerequisites, config inspection, service/judge requirements, SFT/reward intersections, and agentic troubleshooting.
add model/env/algorithm/reward/worker/runner, register_model, RLINF_EXT_MODULE, SupportedEnvType, policy loss/advantage registry, install/Docker/CI/docs/e2e updates, contributor policy	extension-development	Extension touchpoints, registries, source-edit recipes, distributed registration pitfalls, install/Docker/CI/docs checklists, style/tests, and contribution rules.
metrics, TensorBoard/W&B/SwanLab, checkpoints, resume, evaluations, profiling, auto-placement, parity/log analysis, replay buffers, checkpoint tools, CI test selection, NCCL/Gloo/CUDA/SGLang/EGL failures	operations-evaluation-debugging	Evaluation planning, run artifact inspection, metric/checkpoint layout, resume, profiling, data/checkpoint utilities, CI/test choice, and runtime failure triage.

Installation and readiness quick check

• First choose the smallest public install target: embodied workflows need an embodied model/env pair, reasoning/agentic workflows need the agentic stack plus a rollout engine/version choice, docs-only work needs only docs dependencies, and reproducible deployments may prefer the documented Docker image family.
• If the user is working in a live RLinf checkout, the setup-and-cluster sub-skill explains how to map that target to the repo-maintained installer selectors without running them by default.
• After activating the chosen public environment, verify with python -c "import rlinf; print(rlinf.__file__)", ray --version, and the bundled scripts/rlinf_public_api_snapshot.py.
• Do not run install commands unless the user has approved environment mutation; use setup-and-cluster to plan the minimum target first.

Repository-wide facts to verify first

• Public package/distribution: rlinf, version 0.4.0 in the snapshot used to build this skill.
• Core runtime: Ray for distributed processes; Hydra/OmegaConf for YAML composition; PyTorch for model/algorithm code.
• Confirmed task types: embodied, embodied_eval, reasoning, reasoning_eval, coding_online_rl, sft, and offline.
• Confirmed training backends: fsdp and megatron.
• Confirmed rollout backends: sglang and vllm.
• Full training/evaluation often needs GPUs or vendor accelerators, model checkpoints, simulator assets, datasets, services, or real hardware; read-only imports and static config checks are not full workflow validation.

Safe operating defaults

• Prefer static YAML inspection, environment probes, artifact audits, and narrow unit/static tests before native e2e or GPU jobs.
• Do not treat missing optional packages as a bug until the selected workflow and install target are known.
• Do not launch original repository examples or scripts as a skill-default action. Use bundled scripts for safe checks and ask before running user-provided launchers.
• When a workflow spans sub-skills, keep one owner: setup handles Ray/placement, task sub-skills handle configs/recipes, operations handles logs/checkpoints/evals, and extension handles source changes.
• For real robots, require explicit operator approval, calibrated hardware, workspace safety, and e-stop readiness before any motion or controller script.

Bundled root references and script

• references/package-overview.md — architecture map, verified routing constants, run assembly flow, and sub-skill ownership.
• references/troubleshooting.md — cross-cutting symptom map and safe triage ladder.
• references/repo-provenance.md — source snapshot and refresh baseline.
• references/repo-routing-metadata.json — structured managed router metadata.
• scripts/rlinf_public_api_snapshot.py — read-only package/API snapshot helper.