| title | Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning |
|---|---|
| authors | Kejian Zhu, Zhuoran Jin, Dongqi Huang, Hongbang Yuan, Yupu Hao, Kang Liu, Jun Zhao |
| date | 2026-08-06 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.03571 |
| affiliation | Chinese Academic of Science Institute of Automation |
| upvotes | 41 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@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},
eprint={2608.03571},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.03571}
}