| Challenge: | Existing approaches to ground LLMs in textual interactions have been limited due to low computational efficiency and limited performance. |
| Approach: | They propose to use Lookahead models to ground LLMs in interactive text-based games to investigate their language grounding capabilities. |
| Outcome: | The proposed model significantly improves training speed and performance relative to the size of the action space. |
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| Approach: | They propose a framework that guides Large Language Models to learn and play IF games systematically. |
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| Challenge: | Existing reinforcement learning frameworks fail to decompose the task and abstract the action autonomously. |
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| Challenge: | evaluating the player experience in a roleplaying game augmented with LLM-generated dialogue remains a major challenge. |
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clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents (2023.emnlp-main)
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| Challenge: | Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. |
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AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
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You Only Look at Screens: Multimodal Chain-of-Action Agents (2024.findings-acl)
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| Challenge: | Text-based games are situated systems where the game agents observe textual descriptions, and generate textual commands to interact with the environment. |
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Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. |
| Approach: | They propose to re-formulate IF game solving as Multi-Passage Reading Comprehension tasks using context-query attention mechanisms and structured prediction to efficiently generate and evaluate action outputs. |
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