VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents

Zirui Wang; Junyi Zhang; Jiaxin Ge; Long Lian; Letian Fu; Lisa Dunlap; Ken Goldberg; XuDong Wang; Ion Stoica; David M. Chan; Sewon Min; Joseph E. Gonzalez

arXiv · 2601.16973

VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents

Zirui Wang·Junyi Zhang·Jiaxin Ge·Long Lian·Letian Fu·Lisa Dunlap·Ken Goldberg·XuDong Wang·Ion Stoica·David M. Chan·Sewon Min·Joseph E. Gonzalez

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Abstract

Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long horizons. We introduce VisGym, a gymnasium of 17 environments for evaluating and training VLMs. The suite spans symbolic puzzles, real-image understanding, navigation, and manipulation, and provides flexible controls over difficulty, input representation, planning horizon, and feedback. We also provide multi-step solvers that generate structured demonstrations, enabling supervised finetuning. Our evaluations show that all frontier models struggle in interactive settings, achieving low success rates in both the easy (46.6%) and hard (26.0%) configurations. Our experiments reveal notable limitations: models struggle to effectively leverage long context, performing worse with an unbounded history than with truncated windows. Furthermore, we find that several text-based symbolic tasks become substantially harder once rendered visually. However, explicit goal observations, textual feedback, and exploratory demonstrations in partially observable or unknown-dynamics settings for supervised finetuning yield consistent gains, highlighting concrete failure modes and pathways for improving multi-step visual decision-making. Code, data, and models can be found at: https://visgym.github.io/.

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Zirui Wang, Junyi Zhang, Jiaxin Ge, Long Lian, Letian Fu, Lisa Dunlap, Ken Goldberg, XuDong Wang, Ion Stoica, David M. Chan, Sewon Min, Joseph E. Gonzalez. 2026-01-23. VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents. https://arxiv.org/abs/2601.16973

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