🧪 Test?View on arXiv
GAMEGO: Training Game-Dev Agents with Synthetic Trajectories Anchored in Real-World Assets
Not specified
game developmentsynthetic datamachine learninglarge language models
2610.06910
Builder Relevance
1h ago80%
Abstract
This paper presents GameGo, a framework that transforms brief game seeds into comprehensive Product Requirements Documents to enhance game development from user queries.
Reality Card
Core Claim
GameGoCoder, trained on GameGoData, outperforms matched baselines and is comparable to frontier models across game development benchmarks.
Method / Result
GameGoData consists of 55,060 development trajectories across various game types.
Limitations
The reliance on synthetic trajectories may limit the generalizability of the results to real-world game development scenarios.
Paper to code
Verified implementation resources so builders can test the paper’s claims instead of stopping at the abstract.
No verified implementation link has been attached yet. AIBuzzHub will keep this panel separate from unverified search results.