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AgentIndex
AReaL logo
AReaL
★ 5.9k
vs
FedML logo
FedML
★ 4.1k

AReaL vs FedML

AReaL: AReaL is a fully asynchronous reinforcement learning system from Tsinghua IIIS and Ant Group for training large reasoning and agentic models. Version 2.0 splits it into training, inference, agent and weight-update microservices, and black-box agent apps can be trained online by pointing their base_url at AReaL.; FedML: FedML is a unified and scalable open-source machine learning library powered by TensorOpera AI, enabling training and deployment of AI jobs anywhere at any scale. It offers holistic support for MLOps, scheduling, and high-performance ML libraries, including federated learning, distributed training, and generative AI functionalities.

01

TL;DR

AReaL logo
AReaLLLM Infra · ★ 5.9k

RL fine-tuning of reasoning models on math and coding tasks

Read AReaL full review →
FedML logo
FedMLLLM Infra · ★ 4.1k

Distributed training and fine-tuning of large models (including LLMs)

Read FedML full review →
02

Side-by-Side Comparison

Field
AReaL logoAReaL
FedML logoFedML
Category
LLM Infra
LLM Infra
Stars
★ 5.9k
★ 4.1k
License
Apache-2.0
Apache-2.0
Install Command
git clone https://github.com/areal-project/AReaL && cd AReaL && pip install uv && uv sync --extra cuda
git clone https://github.com/FedML-AI/FedML.git
Updated
1d ago
1y ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agent, agent-framework, llm
Federated Learning, MLOps, Distributed Training
03

Features

AReaL logoAReaL
01Fully asynchronous RL training, reported 2.77x faster than synchronous systems in v0.3
02Online RL for black-box agents by replacing base_url and api_key
03Microservice architecture in 2.0 with separate training, inference, agent and weight-update services
04SGLang or vLLM inference backends, Ray clusters and Ascend NPU support
05Examples for math, SWE, search agents, tau2-bench and OpenClaw agents
FedML logoFedML
01Unified and scalable ML library
02Support for Generative AI and LLMs (fine-tuning, deployment)
03Federated Learning platform (on-device, cross-cloud)
04Distributed Training for large and foundational models
05Model serving platform for high scalability and low latency
04

Use Cases

AReaL logoAReaL
↳RL fine-tuning of reasoning models on math and coding tasks
↳Training multi-turn agents such as search or customer-service agents
↳Continuously improving an existing agent app via online RL
FedML logoFedML
↳Distributed training and fine-tuning of large models (including LLMs)
↳Scalable deployment and serving of AI models
↳Federated learning across various decentralized environments
05

Best For

AReaL logoAReaL
EssentialTrending
FedML logoFedML
Essential
FAQ

FAQ

What is the difference between AReaL and FedML?
Both AReaL and FedML are in the LLM Infra category. AReaL has 5.9k stars, while FedML has 4.1k stars.
Which is better, AReaL or FedML?
The best choice depends on your use case. Choose AReaL if RL fine-tuning of reasoning models on math and coding tasks, and FedML if Distributed training and fine-tuning of large models (including LLMs).
Is AReaL free or open source?
Yes, AReaL is open source on GitHub (Apache-2.0).
Is FedML free or open source?
Yes, FedML is open source on GitHub (Apache-2.0).
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Related

Alternatives to AReaL →Alternatives to FedML →AReaL details →FedML details →