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TextArena
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TextArena is an open-source collection of competitive text-based games for training and evaluation of agentic behavior in Large Language Models (LLMs). It spans 57+ unique environments (including single-player, two-player, and multi-player setups) and allows for easy evaluation of model capabilities via an online-play system (against humans and other submitted models) with real-time TrueSkill scores. Traditional benchmarks rarely assess dynamic social skills such as negotiation, theory of mind, and deception, creating a gap that TextArena addresses. Designed with research, community and extensibility in mind, TextArena emphasizes ease of adding new games, adapting the framework, testing models, playing against the models, and training models. Detailed documentation of environments, games, leaderboard, and examples are available on https://github.com/LeonGuertler/TextArena and https://www.textarena.ai/.
Forward citations
Cited by 9 Pith papers
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Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24+
Across 30 languages, commercial LLMs outscore all open-weight models in every EU language, and non-English service costs more and scores lower, suggesting equality requires resources beyond public web crawls.
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CAST: Game Solvers as Turn-Level Teachers for LLM Agents
CAST converts a game solver's per-action cost-to-go changes into turn-level RL credits for LLM agents and reports gains over outcome-only RLVR on three games plus zero-shot transfer to ALFWorld and WebShop.
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LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks
Training a language model by distilling a coach's written experiential knowledge beats training on a scalar rubric score for open-ended tasks, with better out-of-distribution transfer.
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GIFT: Games as Informal Training for Generalizable LLMs
Game-based RL with formal math improves average general-benchmark scores in several settings, but the proposed nested training objective is mathematically the same average-reward objective as mixed training and in-dom...
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Assessing Adaptive World Models in Machines with Novel Games
The paper proposes a framework called world model induction and a novel-game benchmark paradigm for evaluating rapid adaptation in AI.
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Multi-Actor Generative Artificial Intelligence as a Game Engine
Generative multi-actor AI platforms can be built on the Entity-Component pattern, treating the environment (Game Master) as a composable entity, so that one library serves simulation, storytelling, and evaluation goals.
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TextAtari: 100K Frames Game Playing with Language Agents
TextAtari is a text-based Atari benchmark for language agents; 7-8B LLMs stay below 10% of human scores in over 90% of tested conditions, and knowledge injection helps more than chain-of-thought.
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Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games
A group, fuse, and retrain recipe for multi-game reinforcement learning lets a 32B model reach near-Claude3.5 performance on several TextArena games, though the headline score is internally inconsistent.
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KORGym: A Dynamic Game Platform for LLM Reasoning Evaluation
KORGym introduces a 51-game, text and visual, multi-turn benchmark with a normalized scoring scheme, and uses it to compare 19 LLMs and 8 VLMs on six reasoning dimensions.
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