# Learning to Play Like Humans: A Framework for LLM Adaptation in Interactive Fiction Games

arXiv: https://arxiv.org/abs/2505.12439 · HTML: https://mlsanity.com/paper/2505.12439/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 18 May 2025.

## Builds on (most cited first, 20 of 20)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents](https://mlsanity.com/paper/2401.13178/) (2024)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Deep Reinforcement Learning with a Natural Language Action Space](https://mlsanity.com/paper/1511.04636/) (2015)
- [Graph Constrained Reinforcement Learning for Natural Language Action Spaces](https://mlsanity.com/paper/2001.08837/) (2020)
- [How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds](https://mlsanity.com/paper/2006.07409/) (2020)
- [Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning](https://mlsanity.com/paper/2010.02386/) (2020)
- [Multi-Stage Episodic Control for Strategic Exploration in Text Games](https://mlsanity.com/paper/2201.01251/) (2022)
- [Pre-trained Language Models as Prior Knowledge for Playing Text-based Games](https://mlsanity.com/paper/2107.08408/) (2021)
- [Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents](https://mlsanity.com/paper/2103.13552/) (2021)
- [Inherently Explainable Reinforcement Learning in Natural Language](https://mlsanity.com/paper/2112.08907/) (2021)
- [Perceiving the World: Question-guided Reinforcement Learning for Text-based Games](https://mlsanity.com/paper/2204.09597/) (2022)
- [Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process](https://mlsanity.com/paper/2402.19350/) (2024)
- [MLD-EA: Check and Complete Narrative Coherence by Introducing Emotions and Actions](https://mlsanity.com/paper/2412.02897/) (2024)

## Built on by (most cited first, 4 of 4)

- [Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse](https://mlsanity.com/paper/2605.09965/) (2026)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [IVIE: A Neuro-symbolic Approach to Incremental and Validated Generation of Interactive Fiction Worlds](https://mlsanity.com/paper/2606.13348/) (2026)
- [Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving](https://mlsanity.com/paper/2610.16133/) (2026)

## Abstract (arXiv)

Interactive Fiction games (IF games) are where players interact through natural language commands. While recent advances in Artificial Intelligence agents have reignited interest in IF games as a domain for studying decision-making, existing approaches prioritize task-specific performance metrics over human-like comprehension of narrative context and gameplay logic. This work presents a cognitively inspired framework that guides Large Language Models (LLMs) to learn and play IF games systematically. Our proposed **L**earning to **P**lay **L**ike **H**umans (LPLH) framework integrates three key components: (1) structured map building to capture spatial and narrative relationships, (2) action learning to identify context-appropriate commands, and (3) feedback-driven experience analysis to refine decision-making over time. By aligning LLMs-based agents' behavior with narrative intent and commonsense constraints, LPLH moves beyond purely exploratory strategies to deliver more interpretable, human-like performance. Crucially, this approach draws on cognitive science principles to more closely simulate how human players read, interpret, and respond within narrative worlds. As a result, LPLH reframes the IF games challenge as a learning problem for LLMs-based agents, offering a new path toward robust, context-aware gameplay in complex text-based environments.