# GAMEGO: Training Game-Dev Agents with Synthetic Trajectories Anchored in Real-World Assets

arXiv: https://arxiv.org/abs/2610.06910 · HTML: https://mlsanity.com/paper/2610.06910/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 14 of 14)

- [SemDeDup: Data-efficient learning at web-scale through semantic deduplication](https://mlsanity.com/paper/2303.09540/) (2023)
- [D4: Improving LLM Pretraining via Document De-Duplication and Diversification](https://mlsanity.com/paper/2308.12284/) (2023)
- [ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation](https://mlsanity.com/paper/2507.04952/) (2025)
- [GameDevBench: Evaluating Agentic Capabilities Through Game Development](https://mlsanity.com/paper/2602.11103/) (2026)
- [GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?](https://mlsanity.com/paper/2606.17861/) (2026)
- [OpenGame: Open Agentic Coding for Games](https://mlsanity.com/paper/2604.18394/) (2026)
- [GUI Agents for Continual Game Generation](https://mlsanity.com/paper/2605.28258/) (2026)
- [WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Games](https://mlsanity.com/paper/2605.17637/) (2026)
- [V-GameGym: Visual Game Generation for Code Large Language Models](https://mlsanity.com/paper/2509.20136/) (2025)
- [GameXpert-Bench: How Far Are Coding Agents from Expert Game Development?](https://mlsanity.com/paper/2608.21833/) (2026)
- [JAMER: Project-Level Code Framework Dataset and Benchmark on Professional Game Engines](https://mlsanity.com/paper/2606.19830/) (2026)
- [MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation](https://mlsanity.com/paper/2604.15309/) (2026)
- [CreativeGame:Toward Mechanic-Aware Creative Game Generation](https://mlsanity.com/paper/2604.19926/) (2026)
- [Cookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generation](https://mlsanity.com/paper/2605.30000/) (2026)

## Abstract (arXiv)

Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities in web front-end execution, with browser-based game generation emerging as a particularly prominent frontier. While previous efforts frequently rely on complex multi-turn workflows or focus on static game evaluation benchmarks, this work targets direct end-to-end real-world game synthesis driven by coding agents. However, generating complex games directly from sparse user queries often forces coding agents to make underspecified assumptions, yielding incomplete mechanics, disconnected gameplay flows, and limited visual aesthetics. To resolve this issue, this paper presents GameGo, a scalable framework that systematically transforms brief game seeds into comprehensive Product Requirements Documents grounded in industry game-development practices. To retain core gameplay constraints without restricting design exploration, GameGo uses task-specific dynamic compression to maximize information density while preserving instruction following. Based on this pipeline, GameGoData is constructed with 55,060 development trajectories across 2D, 2.5D, and 3D games, alongside GameGoBench, a benchmark comprising 124 diverse game queries. Training GameGoCoder on GameGoData yields a model that outperforms matched baselines and is comparable to frontier models across gamedev benchmarks. All code, datasets, and models will be made publicly available.