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TextArena
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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 19 Pith papers
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Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games
RNG-Bench evaluates MLLMs on hidden-observation reconstruction in non-Markov games, finds forgetting as the dominant error source, and shows fine-tuning on optimal rollouts improves performance with transfer to other ...
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When Context Returns: Toward Robust Internalization in On-Policy Distillation
A stop-gradient consistency regularizer mitigates context-induced degradation in on-policy distillation, improving robustness across 12 configurations.
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Mitigating Misalignment Contagion by Steering with Implicit Traits
Steering language models with intermittent implicit trait reinforcements reduces misalignment contagion in multi-agent social dilemma games more effectively than system prompt repetition.
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Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
Terminal-Bench 2.0 is a new benchmark of 89 realistic terminal tasks on which frontier AI agents score below 65%.
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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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MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs
Mindgames introduces a four-game evaluation platform for multi-agent LLM reasoning, runs a 944-agent competition, surfaces rule-adherence and error-survival limitations, and releases a 29k-game dataset with an offline...
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DemoEvolve: Overcoming Sparse Feedback in Agentic Harness Evolution with Demonstrations
DemoEvolve bootstraps harness evolution with demonstrations to achieve more stable and effective edits than self-rollout search in sparse-feedback environments like Balatro.
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Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale
Presents Hack-Verifiable TextArena, a benchmark that embeds verifiable reward hacking opportunities into environments to enable deterministic measurement of exploitation by language models.
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Mitigating Misalignment Contagion by Steering with Implicit Traits
Language models show misalignment contagion in multi-agent games that is better controlled by intermittently reinforcing initial traits than by system prompt repetition.
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Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actions
LLMs encode accurate but brittle internal beliefs about latent game states and convert them poorly into actions, creating systematic gaps that explain strategic failures.
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Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environm...
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Robots Need More than VLA and World Models
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
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Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse
The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.
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Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents
DEPT detects training impasses in social language agents via dual-scale divergence and entropy, then uses asymmetric reshaping to restore exploration gradients and prevent policy homogenization.
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Gym-V: A Unified Vision Environment System for Agentic Vision Research
Gym-V supplies 179 visual environments showing that observation scaffolding like captions and rules matters more for training success than the choice of RL algorithm.
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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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Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse
This work traces four eras of generalist game players across dataset, model, harness, and benchmark pillars and charts a five-level roadmap ending in agents that create and evolve within game multiverses.
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A Survey of Reinforcement Learning for Large Reasoning Models
A survey compiling RL methods, challenges, data resources, and applications for enhancing reasoning in large language models and large reasoning models since DeepSeek-R1.
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