@inproceedings{zhang-etal-2026-internalizing,
title = "Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning",
author = "Zhang, Xuan and
Zhou, Zhijian and
Qiao, Lingfeng and
Qin, Yulei and
Li, Ke and
Sun, Xing and
Tan, Xiaoyu and
Qu, Chao and
Qi, Yuan",
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.698/",
pages = "16068--16118",
ISBN = "979-8-89176-459-0",
abstract = "Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remain fundamentally reactive in long-horizon tasks. Unlike humans who employ ``what-if'' reasoning to evaluate potential plans before commitment, standard agents lack an internal world model to simulate future outcomes. Therefore, we propose to internalize future-aware planning by training a single autoregressive model to verbalize both a prospective state rollout and a plan-conditioned success estimate{---}a textual analogue of the $Q$-value. Crucially, we identify a format-capability gap: simply fine-tuning agents on look-ahead traces during post-training leads to superficial mimicry of foresight without genuine predictive grounding. To bridge this gap, we introduce a three-stage training paradigm: (i) World Model Agentic Mid-Training (WM-AMT) to inject latent predictive capabilities into the policy; (ii) Format-Eliciting SFT (FE-SFT) to structure this injected capability; and (iii) Foresight-Conditioned Reinforcement Learning (FC-RL) to refine the calibration and utility of the generated simulations. Evaluated on search and mathematical reasoning tasks, our approach consistently outperforms other training baselines. Our results demonstrate that effective internal world modeling in LLM agents requires a capability-first training pipeline to achieve grounded and calibrated foresight."
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<abstract>Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remain fundamentally reactive in long-horizon tasks. Unlike humans who employ “what-if” reasoning to evaluate potential plans before commitment, standard agents lack an internal world model to simulate future outcomes. Therefore, we propose to internalize future-aware planning by training a single autoregressive model to verbalize both a prospective state rollout and a plan-conditioned success estimate—a textual analogue of the Q-value. Crucially, we identify a format-capability gap: simply fine-tuning agents on look-ahead traces during post-training leads to superficial mimicry of foresight without genuine predictive grounding. To bridge this gap, we introduce a three-stage training paradigm: (i) World Model Agentic Mid-Training (WM-AMT) to inject latent predictive capabilities into the policy; (ii) Format-Eliciting SFT (FE-SFT) to structure this injected capability; and (iii) Foresight-Conditioned Reinforcement Learning (FC-RL) to refine the calibration and utility of the generated simulations. Evaluated on search and mathematical reasoning tasks, our approach consistently outperforms other training baselines. Our results demonstrate that effective internal world modeling in LLM agents requires a capability-first training pipeline to achieve grounded and calibrated foresight.</abstract>
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%0 Conference Proceedings
%T Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning
%A Zhang, Xuan
%A Zhou, Zhijian
%A Qiao, Lingfeng
%A Qin, Yulei
%A Li, Ke
%A Sun, Xing
%A Tan, Xiaoyu
%A Qu, Chao
%A Qi, Yuan
%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 zhang-etal-2026-internalizing
%X Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remain fundamentally reactive in long-horizon tasks. Unlike humans who employ “what-if” reasoning to evaluate potential plans before commitment, standard agents lack an internal world model to simulate future outcomes. Therefore, we propose to internalize future-aware planning by training a single autoregressive model to verbalize both a prospective state rollout and a plan-conditioned success estimate—a textual analogue of the Q-value. Crucially, we identify a format-capability gap: simply fine-tuning agents on look-ahead traces during post-training leads to superficial mimicry of foresight without genuine predictive grounding. To bridge this gap, we introduce a three-stage training paradigm: (i) World Model Agentic Mid-Training (WM-AMT) to inject latent predictive capabilities into the policy; (ii) Format-Eliciting SFT (FE-SFT) to structure this injected capability; and (iii) Foresight-Conditioned Reinforcement Learning (FC-RL) to refine the calibration and utility of the generated simulations. Evaluated on search and mathematical reasoning tasks, our approach consistently outperforms other training baselines. Our results demonstrate that effective internal world modeling in LLM agents requires a capability-first training pipeline to achieve grounded and calibrated foresight.
%U https://aclanthology.org/2026.emnlp-main.698/
%P 16068-16118
Markdown (Informal)
[Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning](https://aclanthology.org/2026.emnlp-main.698/) (Zhang et al., EMNLP 2026)
ACL
- Xuan Zhang, Zhijian Zhou, Lingfeng Qiao, Yulei Qin, Ke Li, Xing Sun, Xiaoyu Tan, Chao Qu, and Yuan Qi. 2026. Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 16068–16118, Budapest, Hungary. Association for Computational Linguistics.