@inproceedings{wu-etal-2026-mobilegym,
title = "{M}obile{G}ym: A Verifiable and Highly Parallel Simulation Platform for Mobile {GUI} Agent Research",
author = "Wu, Dingbang and
Hao, Rui and
Wang, Haiyang and
Wu, Shuzhe and
Xiao, Han and
Li, Zhenghong and
Zhou, Bojiang and
Ju, Zheng and
Liu, Zichen and
Fan, Lue and
Zhang, Zhaoxiang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1009/",
pages = "23223--23247",
ISBN = "979-8-89176-459-0",
abstract = "We present MobileGym, a browser-hosted, lightweight, fully controllable simulation platform for everyday mobile use, targeting interaction fidelity without replicating proprietary backends. It enables two capabilities previously out of reach for everyday apps: verifiable outcome signals through deterministic state-based judging over structured JSON state, and scalable online RL through low-cost parallel rollouts. The full environment state is captured, configured, forked, and compared as structured JSON, and a single server can host hundreds of parallel instances (400 MB each, 3 s cold start). A layered state model and a declarative task-definition framework keep state programmability and task creation practical at scale, and a single programmatic judging mechanism delivers both deterministic evaluation verdicts and dense RL rewards. The accompanying MobileGym-Bench provides 416 parameterized task templates (256 test + 160 train) over 28 apps, with deterministic judges and a structured AnswerSheet protocol that avoids free-text matching failures. In a Sim-to-Real case study, GRPO on Qwen3-VL-4B-Instruct gains +12.8 pt on the 256-task test set, and on a 59-task real-device signal subset, real-device execution retains 95.1{\%} of the simulation-side training gain."
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<abstract>We present MobileGym, a browser-hosted, lightweight, fully controllable simulation platform for everyday mobile use, targeting interaction fidelity without replicating proprietary backends. It enables two capabilities previously out of reach for everyday apps: verifiable outcome signals through deterministic state-based judging over structured JSON state, and scalable online RL through low-cost parallel rollouts. The full environment state is captured, configured, forked, and compared as structured JSON, and a single server can host hundreds of parallel instances (400 MB each, 3 s cold start). A layered state model and a declarative task-definition framework keep state programmability and task creation practical at scale, and a single programmatic judging mechanism delivers both deterministic evaluation verdicts and dense RL rewards. The accompanying MobileGym-Bench provides 416 parameterized task templates (256 test + 160 train) over 28 apps, with deterministic judges and a structured AnswerSheet protocol that avoids free-text matching failures. In a Sim-to-Real case study, GRPO on Qwen3-VL-4B-Instruct gains +12.8 pt on the 256-task test set, and on a 59-task real-device signal subset, real-device execution retains 95.1% of the simulation-side training gain.</abstract>
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%0 Conference Proceedings
%T MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research
%A Wu, Dingbang
%A Hao, Rui
%A Wang, Haiyang
%A Wu, Shuzhe
%A Xiao, Han
%A Li, Zhenghong
%A Zhou, Bojiang
%A Ju, Zheng
%A Liu, Zichen
%A Fan, Lue
%A Zhang, Zhaoxiang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wu-etal-2026-mobilegym
%X We present MobileGym, a browser-hosted, lightweight, fully controllable simulation platform for everyday mobile use, targeting interaction fidelity without replicating proprietary backends. It enables two capabilities previously out of reach for everyday apps: verifiable outcome signals through deterministic state-based judging over structured JSON state, and scalable online RL through low-cost parallel rollouts. The full environment state is captured, configured, forked, and compared as structured JSON, and a single server can host hundreds of parallel instances (400 MB each, 3 s cold start). A layered state model and a declarative task-definition framework keep state programmability and task creation practical at scale, and a single programmatic judging mechanism delivers both deterministic evaluation verdicts and dense RL rewards. The accompanying MobileGym-Bench provides 416 parameterized task templates (256 test + 160 train) over 28 apps, with deterministic judges and a structured AnswerSheet protocol that avoids free-text matching failures. In a Sim-to-Real case study, GRPO on Qwen3-VL-4B-Instruct gains +12.8 pt on the 256-task test set, and on a 59-task real-device signal subset, real-device execution retains 95.1% of the simulation-side training gain.
%U https://aclanthology.org/2026.emnlp-main.1009/
%P 23223-23247
Markdown (Informal)
[MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research](https://aclanthology.org/2026.emnlp-main.1009/) (Wu et al., EMNLP 2026)
ACL
- Dingbang Wu, Rui Hao, Haiyang Wang, Shuzhe Wu, Han Xiao, Zhenghong Li, Bojiang Zhou, Zheng Ju, Zichen Liu, Lue Fan, and Zhaoxiang Zhang. 2026. MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 23223–23247, Budapest, Hungary. Association for Computational Linguistics.