Distilling Game Code World Model Generation into Lightweight Large Language Models
The paper discusses the generation of Game Code World Models (GameCWMs) using Large Language Models (LLMs). It presents a method to distill the capabilities of generating game environments into smaller models, enhancing accessibility and scalability. The authors introduce a dataset and a verification framework to improve the generation process, demonstrating increased correctness and adherence to game rules.
- ▪Game Code World Models (GameCWMs) are designed to translate game rules into executable code.
- ▪The authors propose a post-training pipeline that combines Supervised Fine-Tuning with Reinforcement Learning to enhance model capabilities.
- ▪A curated dataset of 30 games is introduced to support the generation of GameCWMs.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24375 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 642s0lFi2ooB · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24375 (cs) [Submitted on 23 May 2026] Title:Distilling Game Code World Model Generation into Lightweight Large Language Models Authors:Tyrone Serapio, Arjun Prakash, Haoyang Xu, Kevin Wang, Amy Greenwald View a PDF of the paper titled Distilling Game Code World Model Generation into Lightweight Large Language Models, by Tyrone Serapio and 4 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have shown great ability in generating executable code from natural language, opening the possibility of automatically constructing environments for AI agents.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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