Scalable Environments Drive Generalizable Agents
The paper discusses the importance of scalable environments for developing generalizable agents in artificial intelligence. It argues that current practices focus too much on task scaling rather than environment scaling, which is crucial for agents to adapt to diverse tasks. The authors propose a taxonomy to differentiate between various scaling methods and emphasize the need for systematic exposure to different executable rule-sets.
- ▪Generalizable agents must adapt to diverse tasks and unseen environments.
- ▪Current scaling practices often leave agents brittle when conditions change.
- ▪The authors propose a unified taxonomy to clarify the challenges of environment scaling.
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Computer Science > Artificial Intelligence arXiv:2605.18181 (cs) [Submitted on 18 May 2026] Title:Scalable Environments Drive Generalizable Agents Authors:Jiayi Zhang, Fanqi Kong, Guibin Zhang, Maojia Song, Zhaoyang Yu, Jianhao Ruan, Jinyu Xiang, Bang Liu, Chenglin Wu, Yuyu Luo View a PDF of the paper titled Scalable Environments Drive Generalizable Agents, by Jiayi Zhang and 9 other authors View PDF HTML (experimental) Abstract:Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such generalization requires environment scaling: expanding the distribution of executable rule-sets that agents interact with, rather than only increasing trajectories or tasks within fixed benchmarks.
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