mlsanityData to 14 Oct 2026

LLMs as Agentic Cooperative Players in Multiplayer UNO

arXiv 2509.09867 ↗First posted 11 Sep 2025cs.AI cs.CLcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

LLMs promise to assist humans -- not just by answering questions, but by offering useful guidance across a wide range of tasks. But how far does that assistance go? Can a large language model based agent actually help someone accomplish their goal as an active participant? We test this question by engaging an LLM in UNO, a turn-based card game, asking it not to win but instead help another player to do so. We built a tool that allows decoder-only LLMs to participate as agents within the RLCard game environment. These models receive full game-state information and respond using simple text prompts under two distinct prompting strategies. We evaluate models ranging from small (1B parameters) to large (70B parameters) and explore how model scale impacts performance. We find that while all models were able to successfully outperform a random baseline when playing UNO, few were able to significantly aid another player.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 15 of its 25 references that are in the map. First posted on arXiv 11 Sep 2025.

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15 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Agents Play Thousands of 3D Video Games17 Mar

Other references · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  6. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  7. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  8. 2019RLCard: A Toolkit for Reinforcement Learning in Card Gamescited by 35
  9. 2023How Powerful are Decoder-Only Transformer Neural Models?cited by 20
  10. 2024Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraftcited by 9
  11. 2024Do Large Language Models Learn Human-Like Strategic Preferences?cited by 8
  12. 2024Chain of Thought Still Thinks Fast: APriCoT Helps with Thinking Slowcited by 8
2 more
  1. 2024PANGeA: Procedural Artificial Narrative using Generative AI for Turn-Based Video Gamescited by 3
  2. 2024The Base-Rate Effect on LLM Benchmark Performance: Disambiguating Test-Taking Strategies from Benchmark Performancecited by 3

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  3. 2025Digital Player: Evaluating Large Language Models based Human-like Agent in Games
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  5. 2024clembench-2024: A Challenging, Dynamic, Complementary, Multilingual Benchmark and Underlying Flexible Framework for LLMs as Multi-Action Agents
  6. 2024Efficacy of Language Model Self-Play in Non-Zero-Sum Games
  7. 2024A Survey on Large Language Model-Based Game Agents
  8. 2025Evaluating LLMs in Open-Source Games