GRLO: Towards Generalizable Reinforcement Learning in Open-Ended Environments from Zero
The paper discusses a new approach to reinforcement learning called GRLO, which aims to enhance generalization in open-ended environments. It demonstrates significant performance improvements with reduced data and compute requirements compared to traditional methods. The authors hope that GRLO will simplify the development of capable post-trained models.
- ▪GRLO improves average performance across all domains from 24.1 to 63.1 using only 5K prompts and 22.7 GPU hours.
- ▪This method requires about $46\times$ less data and $68\times$ less compute than a strong in-domain RLVR baseline.
- ▪The resulting model competes well with Qwen's released post-trained models, which had much larger training costs.
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Computer Science > Machine Learning arXiv:2605.15464 (cs) [Submitted on 14 May 2026] Title:GRLO: Towards Generalizable Reinforcement Learning in Open-Ended Environments from Zero Authors:Shangjian Yin, Yu Fu, Yue Dong, Zhouxing Shi View a PDF of the paper titled GRLO: Towards Generalizable Reinforcement Learning in Open-Ended Environments from Zero, by Shangjian Yin and 3 other authors View PDF HTML (experimental) Abstract:Post-training has become a crucial step for unlocking the capabilities of large language models, with reinforcement learning (RL) emerging as a critical paradigm.
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