Distilling Game Code World Model Generation into Lightweight Large Language Models
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. Recent work on Code World Models (CWMs) demonstrates that LLMs can translate game rules into Python implementations compatible with solvers like Monte Carlo Tree Search. We study this problem in game settings, where generated environments must implement rules, legal actions, state transitions, observations, and rewards. We refer to these game-specific executable models as Game Code World Models (GameCWMs). However, current approaches to generating code world models rely on frontier models and inference-...
| Authors | Tyrone Serapio, Arjun Prakash, Haoyang Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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