AReaL is an infrastructure system for training large language models with reinforcement learning, connecting model training to applications built around AI agents. Researchers and developers use it to train reasoning and agentic models through asynchronous workflows, and the catalogue add-ons support working with AReaL.
Borrowing it
Nothing to install: this file belongs to areal-project/AReaL. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/areal-project/AReaL/main/CLAUDE.mdgit clone --depth 1 https://github.com/areal-project/AReaLWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/areal-project/areal/claude-md)<a href="https://agentmods.dev/instructions/areal-project/areal/claude-md"><img src="https://agentmods.dev/badge/instructions/areal-project/areal/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/areal-project/areal/claude-md"><img src="https://agentmods.dev/badge/instructions/areal-project/areal/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01713 | $0.01713 |
| Opus 5.5 | $0.00685 | $0.00685 |
| Sonnet 5.5 | $0.00343 | $0.00343 |
| Haiku 4.5 | $0.00171 | $0.00171 |
Grade A, and why
AReaL CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 14d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md - AReaL
WHAT: Project Overview
AReaL is a distributed RL training framework for LLM alignment via reinforcement learning.
Tech Stack: Python 3.12+ | PyTorch | FSDP2/Megatron | SGLang/vLLM
Core Directories:
areal/- Core packageapi/- Config dataclasses, workflow/engine contractsengine/- FSDP2, Megatron, SGLang/vLLM adaptersfsdp_utils/- FSDP2-specific utilities (checkpoint, grad, optimizer, parallel)megatron_utils/- Megatron/FP8 utilities (checkpoint, pipeline, quantization)core/- Engine-shared utilities (distributed, lock, model, offload)
infra/- Infrastructure (launcher, scheduler, RPC)utils/- Infrastructure utilities (launcher, proc, http, concurrent, slurm, ray)
workflow/- RolloutWorkflow implementationsreward/- Reward functionsdataset/- Dataset loadersutils/- Cross-cutting utilities (logging, data, checkpoints, network, RL functional)
examples/- Training scripts and configsdocs/- Jupyter Book source
WHY: Purpose
- Enable efficient RL training for LLM alignment at scale
- Async rollout + distributed training for high throughput
- Modular design: workflows, engines, rewards, and datasets are independently extensible
HOW: Core Commands
# Check environment
python --version # Requires 3.12+
uv --version # Install: https://docs.astral.sh/uv/
# Sync dependencies
uv sync --extra cuda # CUDA + SGLang inference (default)
# For vLLM: cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda
uv sync --group dev # Include dev/test packages
uv run python3 areal/tools/validate_installation.py # Validate installation
# Pre-commit hooks
pre-commit install --install-hooks # Set up hooks (run once)
pre-commit run --all-files # Format and lint
# Run tests
# First check GPU availability (many tests require GPU)
python -c "import torch; print('GPU available:', torch.cuda.is_available())"
uv run pytest tests/test_<topic>.py
# Generate CLI docs
uv run python docs/generate_cli_docs.py
# Build docs (canonical, release-aligned)
./docs/build_all.sh
# Do NOT use `jupyter-book build docs/en|docs/zh` directly for final preview/release,
# because it skips AReaL-specific static setup and output packaging.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 14d ago Changed · +1 lines · +16 tokens per session 80afb5768b68
- 2mo ago First seen · 171 lines · 1,697 tokens per session scan A a7a812f4d120
AReaL CLAUDE.md is an instructions file published in the GitHub repository areal-project/AReaL (5,888 stars, last pushed today), licensed Apache-2.0. It adds 1,713 tokens to every session, about $0.0069 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
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AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
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Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
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AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.