AgentIndex icon
AgentIndex
AReaL logo
AReaL
★ 5.9k
vs
rllm logo
rllm
★ 5.9k

AReaL vs rllm

AReaL: 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.; rllm: rLLM is an open-source framework designed for post-training language agents using reinforcement learning. It allows users to easily build, train, and deploy custom agents and environments for real-world workloads.

01

TL;DR

AReaL logo
AReaLLLM Infra · ★ 5.9k

Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks.

Read AReaL full review →
rllm logo
rllmLLM Infra · ★ 5.9k

Training powerful coding models for tasks like code generation and bug fixing.

Read rllm full review →
02

Side-by-Side Comparison

Field
AReaL logoAReaL
rllm logorllm
Category
LLM Infra
LLM Infra
Stars
★ 5.9k
★ 5.9k
License
Apache-2.0
Apache-2.0
Install Command
pip install -e ".[dev,docs]"
uv pip install "rllm[verl] @ git+https://github.com/rllm-org/rllm.git"
Updated
1d ago
1w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
Reinforcement Learning, Large Language Models, Asynchronous Systems
Reinforcement Learning, Language Agents, LLM
03

Features

AReaL logoAReaL
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.
rllm logorllm
01Open-source framework for reinforcement learning-based post-training of language agents.
02Supports building, training, and deploying custom agents and environments.
03Offers multiple training backends including 'verl' and 'tinker'.
04Enables LoRA and VLM training for advanced models.
05Includes AgentWorkflowEngine for training over arbitrary agentic programs.
04

Use Cases

AReaL logoAReaL
↳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.
rllm logorllm
↳Training powerful coding models for tasks like code generation and bug fixing.
↳Developing sophisticated software engineering agents for automated tasks.
↳Building and evaluating multi-agent systems using reinforcement learning techniques.
05

Best For

AReaL logoAReaL
Trending
rllm logorllm
Trending
FAQ

FAQ

What is the difference between AReaL and rllm?
Both AReaL and rllm are in the LLM Infra category. AReaL has 5.9k stars, while rllm has 5.9k stars.
Which is better, AReaL or rllm?
The best choice depends on your use case. Choose AReaL if Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks., and rllm if Training powerful coding models for tasks like code generation and bug fixing..
Is AReaL free or open source?
Yes, AReaL is open source on GitHub (Apache-2.0).
Is rllm free or open source?
Yes, rllm is open source on GitHub (Apache-2.0).
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Related

Alternatives to AReaL →Alternatives to rllm →AReaL details →rllm details →