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TALES: Text Adventure Learning Environment Suite

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arxiv 2504.14128 v4 pith:AKRZUMOP submitted 2025-04-19 cs.AI cs.CL

classification cs.AIcs.CL
keywords reasoningdiversegamescapabilitiesdesignedllmsmodelsperforming
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24+

    cs.CL 2026-08 conditional novelty 7.0 of 10

    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.

  2. Sample-Efficient Learning from Agent Experience

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Agents can consolidate in-context trial-and-error learning into their weights by distilling one-step teacher decisions at recorded histories, needing no additional environment interaction.

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