Researchers Kejian Zhu et al. investigate the limitations of scaling multimodal environment pools for agent training, discovering that simply increasing the quantity of environments does not guarantee improved agent performance. To address these inefficiencies, the authors propose a dual-dimensional framework focused on environment distribution design: diversity and difficulty structure. To enhance diversity, they introduce Ability-aware Environment Selection (AES), a method designed to curate more effective and varied environment sets for training. To optimize the learning trajectory, they propose a Hierarchical Difficulty Curriculum (HDC), which structures curriculum learning through two distinct mechanisms: harness weakening and state-scale progression. By moving beyond naive scaling and focusing on the qualitative distribution of tasks, the study demonstrates that the integration of AES and HDC significantly improves the training efficacy of multimodal agents. This approach suggests that the strategic organization of environmental complexity and task variety is more critical to agent development than the mere expansion of the environment pool size.