# From Word to World: Can Large Language Models be Implicit Text-based World Models?

arXiv: https://arxiv.org/abs/2512.18832 · HTML: https://mlsanity.com/paper/2512.18832/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 31 of its 41 references that are in the map.
- First posted on arXiv 21 Dec 2025.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 30 of 31)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [ScienceWorld: Is your Agent Smarter than a 5th Grader?](https://mlsanity.com/paper/2203.07540/) (2022)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning](https://mlsanity.com/paper/2505.16421/) (2025)
- [Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents](https://mlsanity.com/paper/2411.06559/) (2024)
- [Training Agents Inside of Scalable World Models](https://mlsanity.com/paper/2509.24527/) (2025)
- [StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models](https://mlsanity.com/paper/2403.07714/) (2024)
- [Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation](https://mlsanity.com/paper/2410.13232/) (2024)
- [AgentGym: Evolving Large Language Model-based Agents across Diverse Environments](https://mlsanity.com/paper/2406.04151/) (2024)
- [Acting Less is Reasoning More! Teaching Model to Act Efficiently](https://mlsanity.com/paper/2504.14870/) (2025)
- [World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training](https://mlsanity.com/paper/2509.24948/) (2025)
- [Simulating Environments with Reasoning Models for Agent Training](https://mlsanity.com/paper/2511.01824/) (2025)
- [Can Language Models Serve as Text-Based World Simulators?](https://mlsanity.com/paper/2406.06485/) (2024)
- [RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments](https://mlsanity.com/paper/2511.07317/) (2025)
- [Pre-Trained Video Generative Models as World Simulators](https://mlsanity.com/paper/2502.07825/) (2025)
- [GigaBrain-0: A World Model-Powered Vision-Language-Action Model](https://mlsanity.com/paper/2510.19430/) (2025)
- [Scaling Agent Learning via Experience Synthesis](https://mlsanity.com/paper/2511.03773/) (2025)
- [RLVR-World: Training World Models with Reinforcement Learning](https://mlsanity.com/paper/2505.13934/) (2025)
- [Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning](https://mlsanity.com/paper/2505.13886/) (2025)
- [Text2World: Benchmarking Large Language Models for Symbolic World Model Generation](https://mlsanity.com/paper/2502.13092/) (2025)
- [World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning](https://mlsanity.com/paper/2503.10480/) (2025)
- [Making Large Language Models into World Models with Precondition and Effect Knowledge](https://mlsanity.com/paper/2409.12278/) (2024)
- [Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary](https://mlsanity.com/paper/2506.00886/) (2025)
- [AutoEnv: Automated Environments for Measuring Cross-Environment Agent Learning](https://mlsanity.com/paper/2511.19304/) (2025)
- [LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment](https://mlsanity.com/paper/2506.06355/) (2025)

## Built on by (most cited first, 30 of 42)

- [Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence](https://mlsanity.com/paper/2604.18292/) (2026)
- [Qwen-AgentWorld: Language World Models for General Agents](https://mlsanity.com/paper/2606.24597/) (2026)
- [WebWorld: A Large-Scale World Model for Web Agent Training](https://mlsanity.com/paper/2602.14721/) (2026)
- [Policy and World Modeling Co-Training for Language Agents](https://mlsanity.com/paper/2606.02388/) (2026)
- [Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond](https://mlsanity.com/paper/2604.22748/) (2026)
- [EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL](https://mlsanity.com/paper/2605.18703/) (2026)
- [Self-Evolving World Models for LLM Agent Planning](https://mlsanity.com/paper/2606.30639/) (2026)
- [Can We Predict Before Executing Machine Learning Agents?](https://mlsanity.com/paper/2601.05930/) (2026)
- [Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models](https://mlsanity.com/paper/2601.08955/) (2026)
- [How Mobile World Model Guides GUI Agents?](https://mlsanity.com/paper/2605.10347/) (2026)
- [Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making](https://mlsanity.com/paper/2606.25421/) (2026)
- [PatchWorld: Gradient-Free Optimization of Executable World Models for Agent Environments](https://mlsanity.com/paper/2605.30880/) (2026)
- [CoMAP: Co-Evolving World Models and Agent Policies for LLM Agents](https://mlsanity.com/paper/2606.02372/) (2026)
- [Aligning Agentic World Models via Knowledgeable Experience Learning](https://mlsanity.com/paper/2601.13247/) (2026)
- [Self-Improving World Modelling with Latent Actions](https://mlsanity.com/paper/2602.06130/) (2026)
- [Bridging the Agent-World Gap: Text World Models for LLM-based Agents](https://mlsanity.com/paper/2606.09032/) (2026)
- [Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning](https://mlsanity.com/paper/2606.27483/) (2026)
- [ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation](https://mlsanity.com/paper/2602.01709/) (2026)
- [Reward Prediction with Factorized World States](https://mlsanity.com/paper/2603.09400/) (2026)
- [From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives](https://mlsanity.com/paper/2607.00918/) (2026)
- [AppDeltaWorld: Transition-Grounded Delta Code World Model for Mobile GUI Agents](https://mlsanity.com/paper/2608.05891/) (2026)
- [MemWM: Memory-Augmented Text-Based World Model](https://mlsanity.com/paper/2608.07107/) (2026)
- [Surprisal-Guided Selection: Compute-Optimal Test-Time Strategies for Execution-Grounded Code Generation](https://mlsanity.com/paper/2602.07670/) (2026)
- [Specification-Driven Generation and Evaluation of Discrete-Event World Models via the DEVS Formalism](https://mlsanity.com/paper/2603.03784/) (2026)
- [Democratizing Tool Learning with Environments Fully Simulated by a Free 8B Language Model](https://mlsanity.com/paper/2604.17739/) (2026)
- [Measuring AI Reasoning: A Guide for Researchers](https://mlsanity.com/paper/2605.02442/) (2026)
- [When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection](https://mlsanity.com/paper/2605.26872/) (2026)
- [Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation](https://mlsanity.com/paper/2606.27681/) (2026)
- [Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL](https://mlsanity.com/paper/2607.16204/) (2026)
- [When Stories Evolve: Benchmarking LLM Storytelling Across Agent Architectures in Open-Ended World Simulations](https://mlsanity.com/paper/2608.15654/) (2026)

## Abstract (arXiv)

Agentic reinforcement learning increasingly relies on experience-driven scaling, yet real-world environments remain non-adaptive, limited in coverage, and difficult to scale. World models offer a potential way to improve learning efficiency through simulated experience, but it remains unclear whether large language models can reliably serve this role and under what conditions they meaningfully benefit agents. We study these questions in text-based environments, which provide a controlled setting to reinterpret language modeling as next-state prediction under interaction. We introduce a three-level framework for evaluating LLM-based world models: (i) fidelity and consistency, (ii) scalability and robustness, and (iii) agent utility. Across five representative environments, we find that sufficiently trained world models maintain coherent latent state, scale predictably with data and model size, and improve agent performance via action verification, synthetic trajectory generation, and warm-starting reinforcement learning. Meanwhile, these gains depend critically on behavioral coverage and environment complexity, delineating clear boundry on when world modeling effectively supports agent learning.