| Challenge: | Large Language Models (LLMs) have shown impressive performance on various tasks, but the underlying process behind predicting the desired next token remains a black box. |
| Approach: | They train a GPT-style autoregressive language model using only the next character prediction objective and then train corresponding model with different layer sizes. |
| Outcome: | The proposed model shows a hint of learning a world model representation of the board positions on a simulated game of checkers and human gameplay dataset. |
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| Challenge: | Recent advances in large language models (LLMs) have pointed towards an alternative approach by leveraging the huge amount of knowledge contained in their pre-training datasets. |
| Approach: | They build and use a benchmark to quantify how well text-based simulators can serve as text-driven world simulators. |
| Outcome: | The proposed benchmark aims to quantify how well language models can serve as world simulators. |
PLM-based World Models for Text-based Games (2022.emnlp-main)
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| Challenge: | a new study shows that pre-trained world models provide a strong base for world models . worldformer is a text-based game environment that can be used to learn world models in text-driven games. |
| Approach: | They propose to use pre-trained language models to build world models in text-based game environments. |
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From Word to World: Can Large Language Models be Implicit Text-based World Models? (2026.acl-long)
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Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin, Dongdong Zhang, Cheng Qian, Zeping Li, Xiaoteng Ma, Guanhua Chen, Heng Ji
| Challenge: | Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time. |
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Text2World: Benchmarking Large Language Models for Symbolic World Model Generation (2025.findings-acl)
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Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, Ping Luo
| Challenge: | Recent studies have encountered limitations in leveraging large language models to generate symbolic world models. |
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Making Large Language Models into World Models with Precondition and Effect Knowledge (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are not inherently designed to model real-world dynamics, but can be induced to perform two critical world model functions: determining the applicability of an action based on a given world state and predicting the resulting world state upon action execution. |
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Remember what you did so you know what to do next (2023.findings-emnlp)
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| Challenge: | Existing studies have shown large language models (LLMs) to be poor fit for a simulated robot to achieve 30 classes of goals. |
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Enhancing Agent Learning through World Dynamics Modeling (2024.findings-emnlp)
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| Challenge: | Existing methods assume that large language models have a complete understanding of their environment, overlooking potential gaps in their grasp of actual world dynamics. |
| Approach: | They propose a framework that discovers world dynamics from a small number of demonstrations, verifies the correctness of these dynamics, and evolves new, advanced dynamics tailored to the current situation. |
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Exploring the Learning Capabilities of Language Models using LEVERWORLDS (2024.emnlp-main)
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| Challenge: | Existing models of stochastic learning involve learning general structure rules and specific properties of the instance. |
| Approach: | They propose a framework that allows the generation of physics-inspired worlds that follow a similar generative process with different distributions and their instances can be expressed in natural language. |
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Language Models are Few-Shot Butlers (2021.emnlp-main)
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| Challenge: | Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. |
| Approach: | They propose a two-stage procedure to learn from a small set of demonstrations and a simple reinforcement learning algorithm to improve by interacting with an environment. |
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Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have revolutionized natural language processing and are limited by high inference time in multilingual settings. |
| Approach: | They propose a training recipe for an assistant model in speculative decoding, which are leveraged to draft and-then its future tokens are verified by the target LLM. |
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