AReaL
★ 5.2k
vs
mini-swe-agent
★ 4.7k
AReaL vs mini-swe-agent
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.; mini-swe-agent: Mini-SWE-agent is a lightweight, 100-line AI agent designed to solve GitHub issues and more, offering a simplified yet performant alternative to larger coding agents. It focuses on minimalism, high performance on benchmarks like SWE-bench, and easy deployment across various environments.
01
TL;DR
Choose AReaL if…Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks.
Choose mini-swe-agent if…Researchers for benchmarking, fine-tuning, or RL experiments without bloat
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.
mini-swe-agent01Minimal code (approx. 100 lines of Python)
02High performance (>74% on SWE-bench verified benchmark)
03Easy deployment and sandboxing (Docker, Podman, Singularity)
04Utilizes only Bash tools, avoiding complex tool-calling interfaces
05Linear history for simplified debugging and fine-tuning
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.
mini-swe-agent↳Researchers for benchmarking, fine-tuning, or RL experiments without bloat
↳Developers who want to own, understand, and modify their AI tools
↳Engineers needing a trivial-to-sandbox and deployable solution anywhere
05
Best For
AReaLTrending
mini-swe-agentTrendingHidden Gem
FAQ
FAQ
What is the difference between AReaL and mini-swe-agent?
Both AReaL and mini-swe-agent are in the LLM Infra category. AReaL has 5.2k stars, while mini-swe-agent has 4.7k stars.
Which is better, AReaL or mini-swe-agent?
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 mini-swe-agent if Researchers for benchmarking, fine-tuning, or RL experiments without bloat.
Is AReaL free or open source?
Yes, AReaL is open source on GitHub.
Is mini-swe-agent free or open source?
Yes, mini-swe-agent is open source on GitHub.
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