AReaL

★ 5.9k

code-act

★ 1.7k

AReaL vs code-act

Q: Which is better, AReaL or code-act?

By GitHub stars, AReaL has more community adoption, but the best choice depends on your specific use case.

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.; code-act: CodeAct unifies LLM agents' actions into an executable code space, enabling dynamic revision and new actions based on execution results. This approach significantly outperforms traditional text and JSON action methods, improving LLM agent success rates on complex tasks.

TL;DR

AReaLLLM Infra · ★ 5.9k

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

Read AReaL full review →

code-actLLM Infra · ★ 1.7k

Developing Advanced LLM Agents: For researchers and developers aiming to build more capable and robust LLM agents that can interact dynamically with environments.

Read code-act full review →

Side-by-Side Comparison

Field

AReaL

code-act

Features

AReaL

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.

code-act

01Unified Action Space via Executable Code: Consolidates LLM agents' actions into a unified, executable code space.

02Dynamic Action Revision: Allows LLM agents to dynamically revise prior actions or emit new actions based on real-time observations.

03Integrated Python Interpreter: Seamlessly integrates with a Python interpreter for code execution.

04Superior Performance: Outperforms widely used alternatives like Text and JSON in LLM agent success rates (up to 20% higher).

05Instruction-Tuned Agents (CodeActAgent): Provides pre-trained CodeActAgent models (Mistral, Llama-2) that excel in out-of-domain agent tasks.

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.

code-act

↳Developing Advanced LLM Agents: For researchers and developers aiming to build more capable and robust LLM agents that can interact dynamically with environments.

↳Automated Code Execution and Problem Solving: For scenarios requiring LLMs to execute code, debug, and iterate on solutions based on execution feedback.

↳Complex Task Automation: For automating multi-turn, complex tasks that benefit from dynamic action revision and tool use.

Best For

AReaL

Trending

code-act

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FAQ

What is the difference between AReaL and code-act?

Both AReaL and code-act are in the LLM Infra category. AReaL has 5.9k stars, while code-act has 1.7k stars.

Which is better, AReaL or code-act?

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 code-act if Developing Advanced LLM Agents: For researchers and developers aiming to build more capable and robust LLM agents that can interact dynamically with environments..

Is AReaL free or open source?

Yes, AReaL is open source on GitHub (Apache-2.0).

Is code-act free or open source?

Yes, code-act is open source on GitHub (MIT).

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Alternatives to AReaL →Alternatives to code-act →AReaL details →code-act details →

TL;DR

Side-by-Side Comparison

Features

Use Cases

Best For

FAQ

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