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AReaL

Large-scale asynchronous reinforcement learning system for training reasoning and agentic LLMs - simple, flexible, and production-ready.

★ 5,656 GitHub Apache-2.0 reinforcement-learningagentsllmtrainingmlopsreasoning AI Agents

Overview

AReaL is a large-scale asynchronous reinforcement learning (RL) system built to bridge foundation model training with modern agent-based applications. Developed by researchers from Tsinghua IIIS and Ant Group, it is optimized for efficiency and scalability, making it ideal for training reasoning and agentic models. Its fully asynchronous RL training paradigm delivers industry-leading throughput, and its flexible design lets you plug in black-box agent applications by simply replacing the base_url.

Requirements

Min vCPU
2
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Affiliate disclosure

Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  areal:
    image: ghcr.io/areal-project/areal:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/areal:/data

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