This work revisits the common paradigm of scaling up environment pools for multimodal agent learning. We find that simply increasing the number of training environments does not always improve performance, and multimodal environments are particularly prone to negative transfer and optimization conflicts. Based on these findings, we argue that <strong>effective environment distributions</strong> should be designed along two dimensions: <strong>diversity and difficulty structure</strong>. We propose <strong>Ability-aware Environment Selection (AES)</strong> to select environments with broad capability coverage, low redundancy, and reduced conflicts, and <strong>Hierarchical Difficulty Curriculum (HDC)</strong> to progressively weaken training scaffolds while increasing state complexity. Our results show that carefully designing the environment distribution can substantially outperform naive environment scaling and lead to better training and generalization.</p>\n","updatedAt":"2026-08-10T06:58:47.155Z","author":{"_id":"650fa426877b574970bc1f0c","avatarUrl":"/avatars/55945605b53f92f715f447aab0b1ee95.svg","fullname":"zkj","name":"JokerJan","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":4,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9262646436691284},"editors":["JokerJan"],"editorAvatarUrls":["/avatars/55945605b53f92f715f447aab0b1ee95.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.03571","authors":[{"_id":"6a754626e1228e04b32381ba","name":"Kejian Zhu","hidden":false},{"_id":"6a754626e1228e04b32381bb","name":"Zhuoran Jin","hidden":false},{"_id":"6a754626e1228e04b32381bc","name":"Dongqi Huang","hidden":false},{"_id":"6a754626e1228e04b32381bd","name":"Hongbang Yuan","hidden":false},{"_id":"6a754626e1228e04b32381be","name":"Yupu Hao","hidden":false},{"_id":"6a754626e1228e04b32381bf","name":"Kang Liu","hidden":false},{"_id":"6a754626e1228e04b32381c0","name":"Jun Zhao","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/650fa426877b574970bc1f0c/ORrWTeC5yItBCu2SpDAzd.png"],"publishedAt":"2026-08-06T00:00:00.000Z","submittedOnDailyAt":"2026-08-10T00:00:00.000Z","title":"Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning","submittedOnDailyBy":{"_id":"650fa426877b574970bc1f0c","avatarUrl":"/avatars/55945605b53f92f715f447aab0b1ee95.svg","isPro":false,"fullname":"zkj","user":"JokerJan","type":"user","name":"JokerJan"},"summary":"Recent works train agents by constructing large-scale multimodal environment pools. However, we find that simply increasing the number of multimodal environments does not always benefit. We further analyze the limitations in current multimodal environment distributions through a series of experiments. Based on these findings, we study how to build more effective training environment distributions from two dimensions: **diversity** and **difficulty structure**. For diversity, we propose **Ability-aware Environment Selection (AES)** to obtain diverse environment sets. For difficulty structure, we propose **Hierarchical Difficulty Curriculum (HDC)**, which organizes curriculum learning through two difficulty levels: harness weakening and state-scale progression. 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Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning
Abstract
Recent works train agents by constructing large-scale multimodal environment pools. However, we find that simply increasing the number of multimodal environments does not always benefit. We further analyze the limitations in current multimodal environment distributions through a series of experiments. Based on these findings, we study how to build more effective training environment distributions from two dimensions: **diversity** and **difficulty structure**. For diversity, we propose **Ability-aware Environment Selection (AES)** to obtain diverse environment sets. For difficulty structure, we propose **Hierarchical Difficulty Curriculum (HDC)**, which organizes curriculum learning through two difficulty levels: harness weakening and state-scale progression. Experiments show that AES and HDC effectively improve multimodal agent training.
Community
This work revisits the common paradigm of scaling up environment pools for multimodal agent learning. We find that simply increasing the number of training environments does not always improve performance, and multimodal environments are particularly prone to negative transfer and optimization conflicts. Based on these findings, we argue that effective environment distributions should be designed along two dimensions: diversity and difficulty structure. We propose Ability-aware Environment Selection (AES) to select environments with broad capability coverage, low redundancy, and reduced conflicts, and Hierarchical Difficulty Curriculum (HDC) to progressively weaken training scaffolds while increasing state complexity. Our results show that carefully designing the environment distribution can substantially outperform naive environment scaling and lead to better training and generalization.
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Cite arxiv.org/abs/2608.03571 in a model README.md to link it from this page.
Cite arxiv.org/abs/2608.03571 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2608.03571 in a Space README.md to link it from this page.
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