Moonshine: Distilling Game Content Generators into Steerable Generative Models
Authors: Yuhe Nie, Michael Middleton, Tim Merino, Nidhushan Kanagaraja, Ashutosh Kumar, Zhan Zhuang, Julian Togelius
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our distilled models with the baseline constructive algorithm. Our analysis of the variety, accuracy, and quality of our generation demonstrates the efficacy of distilling constructive methods into controllable text-conditioned PCGML models. The outputs of these models are evaluated through both quantitative and qualitative metrics. |
| Researcher Affiliation | Academia | 1New York University Game Innovation Lab, Brooklyn, New York, USA 2Southern University of Science and Technology, Shenzhen, China 3City University of Hong Kong, Kowloon, Hong Kong SAR China |
| Pseudocode | No | The paper describes the architecture of the Five-Dollar Model and Discrete Diffusion Model, and the steps for synthetic data generation, but does not present them in structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions that the dataset is open-sourced at huggingface.co/datasets/Dolphin Nie/dungeon-dataset, but it does not provide a concrete access link or explicit statement for the source code of the methodology itself. |
| Open Datasets | Yes | Datasets huggingface.co/datasets/Dolphin Nie/dungeon-dataset |
| Dataset Splits | Yes | We create a dataset of maps from the game, split into 49,000 training points, 14,000 test points, and 7,000 validation points. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of specific models like 'gte-large-en-v1.5' and 'Open AI s GPT-4 Turbo (gpt-4-turbo-2024-04-09)' but does not provide specific version numbers for ancillary software libraries or frameworks used in their implementation. |
| Experiment Setup | No | The paper describes the model architectures and loss functions used for the Five-Dollar Model and Discrete Diffusion Model, but it does not provide specific hyperparameter values such as learning rates, batch sizes, number of epochs, or optimizer settings for training. |