AReaL
★ 5.2k
vs
rllm
★ 5.6k
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
Choose AReaL if…Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks.
Choose rllm if…Training powerful coding models for tasks like code generation and bug fixing.
02
Side-by-Side Comparison
03
Features
AReaL01Fully 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.
rllm01Open-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↳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↳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
AReaLTrending
rllmTrending
FAQ
FAQ
What is the difference between AReaL and rllm?
Both AReaL and rllm are in the LLM Infra category. AReaL has 5.2k stars, while rllm has 5.6k 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.
Is rllm free or open source?
Yes, rllm is open source on GitHub (Apache-2.0).
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