# TextWorldExpress: Simulating Text Games at One Million Steps Per Second

arXiv: https://arxiv.org/abs/2208.01174 · HTML: https://mlsanity.com/paper/2208.01174/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 40 references that are in the map.
- First posted on arXiv 1 Aug 2022.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [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)
- [MineRL: A Large-Scale Dataset of Minecraft Demonstrations](https://mlsanity.com/paper/1907.13440/) (2019)
- [The NetHack Learning Environment](https://mlsanity.com/paper/2006.13760/) (2020)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Keep CALM and Explore: Language Models for Action Generation in Text-based Games](https://mlsanity.com/paper/2010.02903/) (2020)
- [Graph Constrained Reinforcement Learning for Natural Language Action Spaces](https://mlsanity.com/paper/2001.08837/) (2020)
- [Language Understanding for Text-based Games Using Deep Reinforcement Learning](https://mlsanity.com/paper/1506.08941/) (2015)
- [Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration](https://mlsanity.com/paper/2002.09253/) (2020)
- [Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning](https://mlsanity.com/paper/1812.01628/) (2018)
- [Semantic Exploration from Language Abstractions and Pretrained Representations](https://mlsanity.com/paper/2204.05080/) (2022)
- [Improving Intrinsic Exploration with Language Abstractions](https://mlsanity.com/paper/2202.08938/) (2022)
- [Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines](https://mlsanity.com/paper/2010.03790/) (2020)
- [Counting to Explore and Generalize in Text-based Games](https://mlsanity.com/paper/1806.11525/) (2018)
- [How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds](https://mlsanity.com/paper/2010.00685/) (2020)
- [Neuro-Symbolic Reinforcement Learning with First-Order Logic](https://mlsanity.com/paper/2110.10963/) (2021)
- [Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning](https://mlsanity.com/paper/2010.02386/) (2020)
- [Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents](https://mlsanity.com/paper/2103.13552/) (2021)
- [Megaverse: Simulating Embodied Agents at One Million Experiences per Second](https://mlsanity.com/paper/2107.08170/) (2021)
- [Exploration Based Language Learning for Text-Based Games](https://mlsanity.com/paper/2001.08868/) (2020)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [A Survey of Text Games for Reinforcement Learning informed by Natural Language](https://mlsanity.com/paper/2109.09478/) (2021)
- [Generalization in Text-based Games via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2109.09968/) (2021)
- [IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents](https://mlsanity.com/paper/2206.00142/) (2022)
- [Playing by the Book: An Interactive Game Approach for Action Graph Extraction from Text](https://mlsanity.com/paper/1811.04319/) (2018)

## Built on by (most cited first, 13 of 13)

- [LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models](https://mlsanity.com/paper/2311.18232/) (2023)
- [PDDLEGO: Iterative Planning in Textual Environments](https://mlsanity.com/paper/2405.19793/) (2024)
- [TALES: Text Adventure Learning Environment Suite](https://mlsanity.com/paper/2504.14128/) (2025)
- [Behavior Cloned Transformers are Neurosymbolic Reasoners](https://mlsanity.com/paper/2210.07382/) (2022)
- [Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents](https://mlsanity.com/paper/2605.06908/) (2026)
- [DAVIS: Planning Agent with Knowledge Graph-Powered Inner Monologue](https://mlsanity.com/paper/2410.09252/) (2024)
- [Language Model as Planner and Formalizer under Constraints](https://mlsanity.com/paper/2510.05486/) (2025)
- [Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games](https://mlsanity.com/paper/2312.04657/) (2023)
- [Structured Event Reasoning with Large Language Models](https://mlsanity.com/paper/2408.16098/) (2024)
- [DORA Explorer: Improving the Exploration Ability of LLMs Without Training](https://mlsanity.com/paper/2604.17244/) (2026)
- [From Agent Failures to Text Policies: What Works and What Breaks](https://mlsanity.com/paper/2607.20668/) (2026)
- [Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems](https://mlsanity.com/paper/2607.27443/) (2026)
- [Self-Evaluating Recursive Agents](https://mlsanity.com/paper/2610.04902/) (2026)

## Abstract (arXiv)

Text-based games offer a challenging test bed to evaluate virtual agents at language understanding, multi-step problem-solving, and common-sense reasoning. However, speed is a major limitation of current text-based games, capping at 300 steps per second, mainly due to the use of legacy tooling. In this work we present TextWorldExpress, a high-performance simulator that includes implementations of three common text game benchmarks that increases simulation throughput by approximately three orders of magnitude, reaching over one million steps per second on common desktop hardware. This significantly reduces experiment runtime, enabling billion-step-scale experiments in about one day.