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

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.