---

name: areal description: "Use AReaL for large-scale asynchronous LLM reinforcement learning, agentic RL, distributed post-training, inference/agent services, backend planning, and customization workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AReaL Repo Skill

AReaL is a Python framework for asynchronous reinforcement learning and post-training of LLM, VLM, and agentic systems. Use this skill when a task asks how to configure, run, customize, or debug AReaL experiments, services, datasets, rewards, workflows, or distributed backends.

This generated skill is self-contained operating guidance. Do not depend on the original checkout being present; use the bundled references and scripts here.

First questions to answer

1. Workflow type: config-driven post-training, custom dataset/reward/workflow, backend/distributed debugging, or v2 service operation?
2. Runtime variant: SGLang CUDA, vLLM CUDA, training-only CUDA, CPU/import-only inspection, sandbox, or vendor accelerator branch?
3. Resource shape: local/Ray/Slurm, nodes, GPUs per node, shared storage, model path, and whether rollout and training roles are separated or colocated?
4. Safety: is this only a command/config review, or may the agent start services, run training, download data/models, or use credentials?

Install and import baseline

AReaL supports Python >=3.11,<3.13. For real training and inference, prefer the project runtime image or a uv environment matching the intended backend variant.

Common install choices:

# Default CUDA runtime: training packages plus SGLang inference.
uv sync --extra cuda

# vLLM variant: use the vLLM project manifests before syncing.
cp pyproject.vllm.toml pyproject.toml
cp uv.vllm.lock uv.lock
uv sync --extra cuda

# CPU/import-oriented development only; not proof of backend runtime.
uv sync

Minimal import smoke after installation:

python - <<'PY'
import areal
from areal.api.cli_args import GRPOConfig, SFTConfig
print(areal.__version__)
print(GRPOConfig.__name__, SFTConfig.__name__)
PY

For a safe environment and CLI check that does not launch training or services, run scripts/areal_env_doctor.py.

Route by task

User task	Read next
Run or adapt GRPO/PPO/SFT/DPO/RW experiments, choose trainer/config class, validate YAML and overrides, checkpoint/logging/recovery	sub-skills/post-training-experiments/SKILL.md
Add or debug datasets, reward functions, RLVRWorkflow, VisionRLVRWorkflow, multi-turn/tool workflows, or agent framework integrations	sub-skills/custom-data-rewards-workflows/SKILL.md
Choose FSDP/Megatron/Archon/SGLang/vLLM backends, parse backend strings, plan GPU allocation, debug CUDA/NCCL/OOM/LoRA/FP8/weight sync	sub-skills/distributed-engines-backends/SKILL.md
Operate areal inf, areal agent, areal train, model registration, service state/logs/status, online RL sessions, Hermes-style loops	sub-skills/services-cli-operations/SKILL.md

Shared references

• references/package-overview.md: AReaL architecture, package layout, public entry points, and installed-package facts.
• references/configuration-cheatsheet.md: shared config classes, override syntax, backend fields, and cross-skill config rules.
• references/troubleshooting.md: cross-cutting install/import, backend variant, config, GPU, service, checkpoint, and credential failure modes.
• references/repo-provenance.md: source snapshot and evidence baseline for refresh decisions.

Operating defaults

• Prefer single-controller mode for new experiments: a Python driver loads a config, creates trainers/controllers, and uses scheduler.type=local|ray|slurm.
• Treat legacy SPMD launchers as compatibility paths. Do not introduce them unless the user explicitly asks for the old mode.
• Prefer proxy-style agent workflows for new agentic RL integrations. Use direct ArealOpenAI only for legacy/framework-specific cases.
• Treat CUDA, SGLang, vLLM, Megatron, Archon, Ray, Slurm, sandbox, and NPU capabilities as backend-specific. A CPU import check does not prove them.
• Never run long training, start services, download model/data artifacts, mutate CUDA/driver stacks, or use credentials without explicit user approval.

Safe bundled scripts

• scripts/areal_env_doctor.py: import/CLI/backend visibility check.
• sub-skills/post-training-experiments/scripts/validate_experiment_config.py: safe config/override validator.
• sub-skills/custom-data-rewards-workflows/scripts/check_workflow_contract.py: dataset/reward/workflow import and sample-contract checker.
• sub-skills/distributed-engines-backends/scripts/check_backend_plan.py: backend-string and GPU-demand checker.
• sub-skills/services-cli-operations/scripts/check_service_cli.py: static service CLI/TOML command checker.

Handoff language

When you cannot verify a capability locally, be explicit:

• "Verified: import/config/CLI surface only."
• "Not verified: full SGLang/vLLM/Megatron/FSDP/Archon runtime because that requires matching CUDA packages, model weights, and/or cluster resources."
• "Next required live check: run the user's approved command in their target environment and capture logs/status/metrics."