Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning

Kejian Zhu, Zhuoran Jin, Dongqi Huang, Hongbang Yuan, Yupu Hao, Kang Liu, Jun Zhao

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning: 47 upvotes on Hugging Face Daily Papers, #4 of 35 papers on 2026-08-10. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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