A guide to building long-term compounding knowledge infrastructure. See details on GitHub .

AReaL

AReaL is an open-source system for large-scale asynchronous reinforcement learning training and inference, designed for LLM inference, agent, and RL training scenarios.

Introduction

AReaL (Asynchronous Reinforcement Learning) is a highly scalable asynchronous RL training system designed for large-scale LLM inference and agent training. It supports multiple training backends, distributed configurations, and modular algorithm components.

Key Features

  • Fully asynchronous training pipeline to improve resource utilization and throughput.
  • Rich integration with training/inference backends (vLLM, Megatron, FSDP, Ray, etc.).
  • Configurable and reproducible toolchain for both research and engineering.

Use Cases

  • Large-scale RLHF / agent training.
  • Algorithm research and rapid prototyping.

Technical Highlights

  • Primarily implemented in Python, with documentation and examples for easy onboarding.

Comments

AReaL
Resource Info
🏋️ Training 🖥️ ML Platform 🌱 Open Source