AReaL is an open-source reinforcement learning dataset developed by Ant Research for training Large Reasoning Models (LRMs). The project, last updated on March 28, 2025, is designed to be reproducible and inclusive, adapting the Open-Source Project ReaLHF. It is part of broader efforts to develop tools for an open AGI ecosystem.
Use Cases
- Training reinforcement learning agents for reasoning tasks based on the described RL system.
- Reproducing and benchmarking large reasoning model performance based on the open-source methodology.
- Contributing to or extending an open-source RL project for AGI development based on the inclusive framework.
Strengths
- Project is fully open-sourced, allowing for community reproduction and contribution.
- Dataset was last updated on 2025-03-28, indicating recent maintenance.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count, file formats, and sample data are unknown, which may limit suitability assessment.
- License information is unavailable, which could restrict commercial or research use.
Provenance
- Source
- Ant Research, RL Lab
- Collection Method
- Inherits and adapts the Open-Source Project ReaLHF; specific data gathering method is not detailed.
- Time Range
- null
- Freshness
- Last updated 2025-03-28 09:49:27; freshness should be verified.
- Geography
- null