Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning
Summary
The paper argues that simply scaling multimodal environments does not always improve agent training, and proposes Ability-aware Environment Selection (AES) and Hierarchical Difficulty Curriculum (HDC) to better structure environment distributions along diversity and difficulty dimensions.
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Paper page - Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning
Source: https://huggingface.co/papers/2608.03571
Abstract
Recentworkstrainagentsbyconstructinglarge-scalemultimodalenvironmentpools.However,wefindthatsimplyincreasingthenumberofmultimodalenvironmentsdoesnotalwaysbenefit.Wefurtheranalyzethelimitationsincurrentmultimodalenvironmentdistributionsthroughaseriesofexperiments.Basedonthesefindings,westudyhowtobuildmoreeffectivetrainingenvironmentdistributionsfromtwodimensions:**diversity**and**difficultystructure**.Fordiversity,wepropose**Ability-awareEnvironmentSelection(AES)**toobtaindiverseenvironmentsets.Fordifficultystructure,wepropose**HierarchicalDifficultyCurriculum(HDC)**,whichorganizescurriculumlearningthroughtwodifficultylevels:harnessweakeningandstate-scaleprogression.ExperimentsshowthatAESandHDCeffectivelyimprovemultimodalagenttraining.
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