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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15464 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Summary source text | contentText |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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