@inproceedings{huang-etal-2026-beyond-state,
title = "Beyond State Consistency: Behavior Consistency in Text-Based World Models",
author = "Huang, Youling and
Chen, Guanqiao and
Yao, Junchi and
Wang, Lu and
Yang, Fangkai and
Du, Chao and
Zhao, ChenZhuo and
Zhao, Pu and
Lin, Qingwei and
Rajmohan, Saravan and
Zhang, Dongmei",
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.233/",
pages = "5339--5359",
ISBN = "979-8-89176-459-0",
abstract = "World models are emerging as critical components for assessing the consequences of actions taken by interactive agents in online planning and offline evaluation. In text-based environments, world models are typically trained and evaluated with single-step metrics such as Exact Match, aiming to improve the similarity between predicted and real-world states, but such metrics have been shown to be insufficient for capturing actual agent behavior. To address this issue, we introduce a new behavior-consistency training paradigm aimed at improving the functional consistency between the world model and the real environment. This paradigm focuses on optimizing a tractable step-level reward named Behavior Consistency Reward (BehR), which measures how much the likelihood of a logged next action changes between the real state and the world-model-predicted state under a frozen Reference Agent. Experiments on WebShop and TextWorld show that BehR-based training improves task-level functional consistency in several settings, with the clearest gains in WebShop and smaller changes in near-ceiling regimes, while preserving or improving single-step prediction quality in three of four settings. BehR-trained world models also reduce false positives in offline surrogate evaluation and improve inference-time lookahead planning over the corresponding supervised world-model baselines. Code: https://github.com/Ricardo-H/behr-wm."
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<abstract>World models are emerging as critical components for assessing the consequences of actions taken by interactive agents in online planning and offline evaluation. In text-based environments, world models are typically trained and evaluated with single-step metrics such as Exact Match, aiming to improve the similarity between predicted and real-world states, but such metrics have been shown to be insufficient for capturing actual agent behavior. To address this issue, we introduce a new behavior-consistency training paradigm aimed at improving the functional consistency between the world model and the real environment. This paradigm focuses on optimizing a tractable step-level reward named Behavior Consistency Reward (BehR), which measures how much the likelihood of a logged next action changes between the real state and the world-model-predicted state under a frozen Reference Agent. Experiments on WebShop and TextWorld show that BehR-based training improves task-level functional consistency in several settings, with the clearest gains in WebShop and smaller changes in near-ceiling regimes, while preserving or improving single-step prediction quality in three of four settings. BehR-trained world models also reduce false positives in offline surrogate evaluation and improve inference-time lookahead planning over the corresponding supervised world-model baselines. Code: https://github.com/Ricardo-H/behr-wm.</abstract>
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%0 Conference Proceedings
%T Beyond State Consistency: Behavior Consistency in Text-Based World Models
%A Huang, Youling
%A Chen, Guanqiao
%A Yao, Junchi
%A Wang, Lu
%A Yang, Fangkai
%A Du, Chao
%A Zhao, ChenZhuo
%A Zhao, Pu
%A Lin, Qingwei
%A Rajmohan, Saravan
%A Zhang, Dongmei
%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 huang-etal-2026-beyond-state
%X World models are emerging as critical components for assessing the consequences of actions taken by interactive agents in online planning and offline evaluation. In text-based environments, world models are typically trained and evaluated with single-step metrics such as Exact Match, aiming to improve the similarity between predicted and real-world states, but such metrics have been shown to be insufficient for capturing actual agent behavior. To address this issue, we introduce a new behavior-consistency training paradigm aimed at improving the functional consistency between the world model and the real environment. This paradigm focuses on optimizing a tractable step-level reward named Behavior Consistency Reward (BehR), which measures how much the likelihood of a logged next action changes between the real state and the world-model-predicted state under a frozen Reference Agent. Experiments on WebShop and TextWorld show that BehR-based training improves task-level functional consistency in several settings, with the clearest gains in WebShop and smaller changes in near-ceiling regimes, while preserving or improving single-step prediction quality in three of four settings. BehR-trained world models also reduce false positives in offline surrogate evaluation and improve inference-time lookahead planning over the corresponding supervised world-model baselines. Code: https://github.com/Ricardo-H/behr-wm.
%U https://aclanthology.org/2026.emnlp-main.233/
%P 5339-5359
Markdown (Informal)
[Beyond State Consistency: Behavior Consistency in Text-Based World Models](https://aclanthology.org/2026.emnlp-main.233/) (Huang et al., EMNLP 2026)
ACL
- Youling Huang, Guanqiao Chen, Junchi Yao, Lu Wang, Fangkai Yang, Chao Du, ChenZhuo Zhao, Pu Zhao, Qingwei Lin, Saravan Rajmohan, and Dongmei Zhang. 2026. Beyond State Consistency: Behavior Consistency in Text-Based World Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 5339–5359, Budapest, Hungary. Association for Computational Linguistics.