AReaL
Active·★ 5.2k·Updated 2026-05-29
★ Trending
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible.
AReaL is an open-source, fully asynchronous reinforcement learning training system designed for large reasoning and agentic models. It offers exceptional flexibility, industry-leading speed, and scalability from a single node to over 1,000 GPUs, achieving state-of-the-art performance.
#Reinforcement Learning#Large Language Models#Asynchronous Systems#Agentic AI#Scalable Training
01
Features
01Fully Asynchronous RL Training: Enables stable, industry-leading speed for reinforcement learning.
02Scalability: Seamlessly adapts from single-node setups to over 1,000 GPUs.
03Flexible Agentic Rollout: Easy customization for multi-turn agentic workflows and integration with external frameworks.
04Cutting-Edge Performance: Achieves state-of-the-art results for math, coding, and search agents.
05Open-Source & Reproducible: Provides full training details, data, and infrastructure to reproduce results.
02
Compatibility
Ascend NPU
Stable Support
Verified via docs
Megatron
Partial Integration
Verified via docs
PyTorch FSDP
Partial Integration
Verified via docs
vLLM
Partial Integration
Verified via docs
SGLang
Partial Integration
Verified via docs
03
Quick start
1
$ pip install -e ".[dev,docs]"
04
Use cases
↳Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks.
↳Large Language Model Alignment (RLHF): Fine-tuning LLMs using Reinforcement Learning from Human Feedback.
↳Multi-Turn Agentic Workflows: Implementing and customizing iterative agent behaviors with self-correction and tool integration.
05
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