Paper Detail
Kejian Zhu, Zhuoran Jin, Dongqi Huang, Hongbang Yuan, Yupu Hao, Kang Liu, Jun Zhao
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.
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@misc{zhu2026beyond,
title = {Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning},
author = {Kejian Zhu and Zhuoran Jin and Dongqi Huang and Hongbang Yuan and Yupu Hao and Kang Liu and Jun Zhao},
year = {2026},
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},
url = {https://huggingface.co/papers/2608.03571},
keywords = {code available, huggingface daily},
eprint = {2608.03571},
archiveprefix = {arXiv},
}
{}