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

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

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ethics.ai (5 August 2026), “Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning,” evidence record 17985, https://ethics.ai/record/17985 (originally published by HuggingFace Daily Papers).

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