Evolving Virtual World with Delta-Engine

• URL: http://arxiv.org/abs/2408.05842v4
• Date: Mon, 2 Sep 2024 15:08:32 GMT
• Title: Evolving Virtual World with Delta-Engine
• Authors: Hongqiu Wu, Zekai Xu, Tianyang Xu, Shize Wei, Yan Wang, Jiale Hong, Weiqi Wu, Hai Zhao, Min Zhang, Zhezhi He,
• Abstract summary: We propose a special engine called textemphDelta-Engine to drive this virtual world. The key feature of the delta-engine is its scalability to unknown elements within the world.
• Score: 60.488864128937955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we focus on the \emph{virtual world}, a cyberspace where people can live in. An ideal virtual world shares great similarity with our real world. One of the crucial aspects is its evolving nature, reflected by individuals' capability to grow and thereby influence the objective world. Such dynamics is unpredictable and beyond the reach of existing systems. For this, we propose a special engine called \textbf{\emph{Delta-Engine}} to drive this virtual world. $\Delta$ associates the world's evolution to the engine's scalability. It consists of a base engine and a neural proxy. The base engine programs the prototype of the virtual world; given a trigger, the neural proxy generates new snippets on the base engine through \emph{incremental prediction}. This paper presents a full-stack introduction to the delta-engine. The key feature of the delta-engine is its scalability to unknown elements within the world, Technically, it derives from the prefect co-work of the neural proxy and the base engine, and the alignment with high-quality data. We introduce an engine-oriented fine-tuning method that embeds the base engine into the proxy. We then discuss the human-LLM collaborative design to produce novel and interesting data efficiently. Eventually, we propose three evaluation principles to comprehensively assess the performance of a delta engine: naive evaluation, incremental evaluation, and adversarial evaluation.

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