AgileRL logo
AgileRL
★ 960
vs
AReaL logo
AReaL
★ 5.9k

AgileRL vs AReaL

AgileRL: AgileRL is a Deep Reinforcement Learning library that streamlines development by introducing RLOps, or MLOps for reinforcement learning. It significantly reduces training time and hyperparameter optimization using pioneering evolutionary techniques, offering up to 10x faster optimization than state-of-the-art methods.; 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.

01

TL;DR

AgileRL logo
AgileRLLLM Infra · ★ 960

Training single-agent tasks in standard Gymnasium environments.

AReaL logo
AReaLLLM Infra · ★ 5.9k

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

02

Side-by-Side Comparison

Field
AgileRL logoAgileRL
AReaL logoAReaL
Category
LLM Infra
LLM Infra
Stars
★ 960
★ 5.9k
License
Apache-2.0
Apache-2.0
Install Command
pip install agilerl
pip install -e ".[dev,docs]"
Updated
1d ago
1d ago
Open Source
Yes
Yes
Tags
Reinforcement Learning, Deep Learning, Hyperparameter Optimization
Reinforcement Learning, Large Language Models, Asynchronous Systems
03

Features

AgileRL logoAgileRL
01RLOps integration for streamlined reinforcement learning development.
02Pioneering evolutionary hyperparameter optimization (HPO) techniques.
03Comprehensive suite of evolvable on-policy, off-policy, offline, multi-agent, and contextual multi-armed bandit algorithms.
04Support for distributed training.
05Algorithms for Large Language Model (LLM) finetuning.
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.
04

Use Cases

AgileRL logoAgileRL
↳Training single-agent tasks in standard Gymnasium environments.
↳Developing multi-agent reinforcement learning solutions in PettingZoo environments.
↳Fine-tuning Large Language Models (LLMs) with reinforcement learning algorithms.
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.
05

Best For

AgileRL logoAgileRL
TrendingHidden Gem
AReaL logoAReaL
Trending
FAQ

FAQ

What is the difference between AgileRL and AReaL?
Both AgileRL and AReaL are in the LLM Infra category. AgileRL has 960 stars, while AReaL has 5.9k stars.
Which is better, AgileRL or AReaL?
The best choice depends on your use case. Choose AgileRL if Training single-agent tasks in standard Gymnasium environments., and AReaL if Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks..
Is AgileRL free or open source?
Yes, AgileRL is open source on GitHub (Apache-2.0).
Is AReaL free or open source?
Yes, AReaL is open source on GitHub (Apache-2.0).
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