# TALES: Text Adventure Learning Environment Suite

arXiv: https://arxiv.org/abs/2504.14128 · HTML: https://mlsanity.com/paper/2504.14128/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 43 references that are in the map.
- First posted on arXiv 19 Apr 2025.

## Builds on (most cited first, 30 of 35)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://mlsanity.com/paper/2407.18901/) (2024)
- [AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents](https://mlsanity.com/paper/2401.13178/) (2024)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents](https://mlsanity.com/paper/2403.02502/) (2024)
- [ADaPT: As-Needed Decomposition and Planning with Language Models](https://mlsanity.com/paper/2311.05772/) (2023)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks](https://mlsanity.com/paper/2305.17390/) (2023)
- [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)
- [Evaluating Cognitive Maps and Planning in Large Language Models with CogEval](https://mlsanity.com/paper/2309.15129/) (2023)
- [Language Understanding for Text-based Games Using Deep Reinforcement Learning](https://mlsanity.com/paper/1506.08941/) (2015)
- [Agent Planning with World Knowledge Model](https://mlsanity.com/paper/2405.14205/) (2024)
- [Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning](https://mlsanity.com/paper/1812.01628/) (2018)
- [How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds](https://mlsanity.com/paper/2006.07409/) (2020)
- [Counting to Explore and Generalize in Text-based Games](https://mlsanity.com/paper/1806.11525/) (2018)
- [Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models](https://mlsanity.com/paper/2405.15143/) (2024)
- [Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions](https://mlsanity.com/paper/2304.02868/) (2023)
- [ECBD: Evidence-Centered Benchmark Design for NLP](https://mlsanity.com/paper/2406.08723/) (2024)
- [Soft Self-Consistency Improves Language Model Agents](https://mlsanity.com/paper/2402.13212/) (2024)
- [ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy](https://mlsanity.com/paper/2403.14589/) (2024)
- [Empowering Large Language Model Agents through Action Learning](https://mlsanity.com/paper/2402.15809/) (2024)
- [NAIL: A General Interactive Fiction Agent](https://mlsanity.com/paper/1902.04259/) (2019)
- [Policy Improvement using Language Feedback Models](https://mlsanity.com/paper/2402.07876/) (2024)
- [A Survey of Text Games for Reinforcement Learning informed by Natural Language](https://mlsanity.com/paper/2109.09478/) (2021)
- [TextWorldExpress: Simulating Text Games at One Million Steps Per Second](https://mlsanity.com/paper/2208.01174/) (2022)
- [Language Guided Exploration for RL Agents in Text Environments](https://mlsanity.com/paper/2403.03141/) (2024)

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

- [WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Games](https://mlsanity.com/paper/2605.17637/) (2026)
- [PuzzleJAX: A Benchmark for Reasoning and Learning](https://mlsanity.com/paper/2508.16821/) (2025)
- [A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench](https://mlsanity.com/paper/2507.08491/) (2025)
- [Sample-Efficient Learning from Agent Experience](https://mlsanity.com/paper/2607.21051/) (2026)
- [Dual-Scale World Models for LLM Agents Towards Hard-Exploration Problems](https://mlsanity.com/paper/2509.24116/) (2025)
- [Code Owns the Simulation, Jev Owns the Evaluation](https://mlsanity.com/paper/2610.01834/) (2026)
- [ByteSized32Refactored: Towards an Extensible Interactive Text Games Corpus for LLM World Modeling and Evaluation](https://mlsanity.com/paper/2509.23979/) (2025)
- [LACE: Lattice Attention for Cross-thread Exploration](https://mlsanity.com/paper/2604.15529/) (2026)
- [DORA Explorer: Improving the Exploration Ability of LLMs Without Training](https://mlsanity.com/paper/2604.17244/) (2026)
- [Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24+](https://mlsanity.com/paper/2608.01395/) (2026)
- [Reading Right, Acting Wrong: Diagnosing Decision Failures of System One Models in Agent Loops](https://mlsanity.com/paper/2610.18579/) (2026)

## Abstract (arXiv)

Reasoning is an essential skill to enable Large Language Models (LLMs) to interact with the world. As tasks become more complex, they demand increasingly sophisticated and diverse reasoning capabilities for sequential decision-making, requiring structured reasoning over the context history to determine the next best action. We introduce TALES, a diverse collection of synthetic and human-written text-adventure games designed to challenge and evaluate diverse reasoning capabilities. We present results over a range of LLMs, open- and closed-weights, performing a qualitative analysis on the top performing models. Despite an impressive showing on synthetic games, even the top LLM-driven agents fail to achieve 15% on games designed for human enjoyment. Code and visualization of the experiments can be found at https://microsoft.github.io/tale-suite.