AReaL
★ 5.2k
vs
xLAM
★ 621
AReaL vs xLAM
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.; xLAM: xLAM is a research repository for Large Action Models (LAMs), which aggregates and unifies agent trajectories from diverse environments into a consistent format. It streamlines the creation of a generic data loader optimized for agent training, enabling robust model development across various scenarios.
01
TL;DR
Choose AReaL if…Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks.
Choose xLAM if…Function calling in LLMs
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.
xLAM01Aggregates agent trajectories from distinct environments
02Standardizes and unifies trajectories into a consistent format
03Optimized generic data loader for agent training
04Maintains equilibrium across different data sources during training
05Supports efficient inference with Transformers and vLLM
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.
xLAM↳Function calling in LLMs
↳Training autonomous agents
↳Multi-turn conversation processing
05
Best For
AReaLTrending
xLAMTrending
FAQ
FAQ
What is the difference between AReaL and xLAM?
Both AReaL and xLAM are in the LLM Infra category. AReaL has 5.2k stars, while xLAM has 621 stars.
Which is better, AReaL or xLAM?
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 xLAM if Function calling in LLMs.
Is AReaL free or open source?
Yes, AReaL is open source on GitHub.
Is xLAM free or open source?
Yes, xLAM is open source on GitHub (APACHE).
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