# Revisiting the Othello World Model Hypothesis

arXiv: https://arxiv.org/abs/2503.04421 · HTML: https://mlsanity.com/paper/2503.04421/

## Key facts

- Cited by 11 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 22 of its 34 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 22 of 22)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task](https://mlsanity.com/paper/2210.13382/) (2022)
- [The Platonic Representation Hypothesis](https://mlsanity.com/paper/2405.07987/) (2024)
- [Emergent Linear Representations in World Models of Self-Supervised Sequence Models](https://mlsanity.com/paper/2309.00941/) (2023)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [Implicit Representations of Meaning in Neural Language Models](https://mlsanity.com/paper/2106.00737/) (2021)
- [Language Models Meet World Models: Embodied Experiences Enhance Language Models](https://mlsanity.com/paper/2305.10626/) (2023)
- [Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color](https://mlsanity.com/paper/2109.06129/) (2021)
- [Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models](https://mlsanity.com/paper/2403.15498/) (2024)
- [Can Language Models Serve as Text-Based World Simulators?](https://mlsanity.com/paper/2406.06485/) (2024)
- [mOthello: When Do Cross-Lingual Representation Alignment and Cross-Lingual Transfer Emerge in Multilingual Models?](https://mlsanity.com/paper/2404.12444/) (2024)
- [Linear Latent World Models in Simple Transformers: A Case Study on Othello-GPT](https://mlsanity.com/paper/2310.07582/) (2023)
- [Structured World Representations in Maze-Solving Transformers](https://mlsanity.com/paper/2312.02566/) (2023)
- [Emergence of Abstract State Representations in Embodied Sequence Modeling](https://mlsanity.com/paper/2311.02171/) (2023)
- [Learning to Play Othello with Deep Neural Networks](https://mlsanity.com/paper/1711.06583/) (2017)

## Built on by (most cited first, 11 of 11)

- [Mechanistic Indicators of Understanding in Large Language Models](https://mlsanity.com/paper/2507.08017/) (2025)
- [MetaOthello: A Controlled Study of Multiple World Models in Transformers](https://mlsanity.com/paper/2602.23164/) (2026)
- [Tool-Call Dependency Structure is Linearly Decodable in LLM Agent Residual Streams](https://mlsanity.com/paper/2605.25310/) (2026)
- [What if Othello-Playing Language Models Could See?](https://mlsanity.com/paper/2507.14520/) (2025)
- [Reasoning Capabilities of Large Language Models. Lessons Learned from General Game Playing](https://mlsanity.com/paper/2602.19160/) (2026)
- [Understanding the Emergence of Seemingly Useless Features in Next-Token Predictors](https://mlsanity.com/paper/2603.14087/) (2026)
- [When 2D Tasks Meet 1D Serialization: On Serialization Friction in Structured Tasks](https://mlsanity.com/paper/2604.27272/) (2026)
- [A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners](https://mlsanity.com/paper/2606.03685/) (2026)
- [Autoregressive Mosaics: Probing 2D Spatial Reasoning in Text-Only Language Models](https://mlsanity.com/paper/2608.30751/) (2026)
- [Few-Shot Demonstrations Elicit the Use of In-Context World Representations in LLMs](https://mlsanity.com/paper/2609.24352/) (2026)
- [Small transformers track Bayesian evidence for latent common causes via a context-invariant mechanism](https://mlsanity.com/paper/2609.35161/) (2026)

## Abstract (arXiv)

Li et al. (2023) used the Othello board game as a test case for the ability of GPT-2 to induce world models, and were followed up by Nanda et al. (2023b). We briefly discuss the original experiments, expanding them to include more language models with more comprehensive probing. Specifically, we analyze sequences of Othello board states and train the model to predict the next move based on previous moves. We evaluate seven language models (GPT-2, T5, Bart, Flan-T5, Mistral, LLaMA-2, and Qwen2.5) on the Othello task and conclude that these models not only learn to play Othello, but also induce the Othello board layout. We find that all models achieve up to 99% accuracy in unsupervised grounding and exhibit high similarity in the board features they learned. This provides considerably stronger evidence for the Othello World Model Hypothesis than previous works.