# ReactiveGWM：在反应式游戏世界模型中引导非玩家角色

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-14 08:00
- AIHOT 分数：56
- AIHOT 链接：https://aihot.virxact.com/items/cmpajrzcm0zjislnzw197ft6r
- 原文链接：https://arxiv.org/abs/2605.15256

## AI 摘要

现有游戏世界模型多从主观玩家视角模拟环境，将非玩家角色（NPC）视为背景像素，难以建模玩家与NPC的动态交互。本文提出ReactiveGWM，该反应式游戏世界模型通过显式解耦玩家控制与NPC行为来解决此问题：玩家动作通过轻量级加性偏置注入扩散主干，而高级NPC响应则通过交叉注意力模块实现。这些模块学习的是与游戏无关的交互逻辑表示，支持零样本策略迁移——学习到的模块可直接插入不同游戏的现成世界模型中，无需针对特定领域重新训练即可实现可引导的NPC交互。在《街头霸王》系列游戏上的评估表明，ReactiveGWM在保持精细玩家可控性的同时，实现了稳健且与提示对齐的NPC策略遵循。

## 正文

Current game world models simulate environments from a subjective, player-centric perspective. However, by treating the Non-Player Character (NPC) merely as background pixels, these models cannot capture interactions between the player and NPC. In that sense, they act as passive video renderers rather than real simulation engines, lacking the physical understanding needed to model action-induced NPC reactivities. We introduce ReactiveGWM, a reactive game world model that synthesizes dynamic interactions between the player and NPC. Instead of entangling all interaction dynamics, ReactiveGWM explicitly decouples player controls from NPC behaviors. Player actions are injected into the diffusion backbone via a lightweight additive bias, while high-level NPC responses (e.g., Offense, Control, Defense) are grounded through cross-attention modules. Crucially, these modules learn a game-agnostic representation of interactive logic. This enables zero-shot strategy transfer: our learned modules can be plugged directly into off-the-shelf, unannotated world models of different games. This instantly unlocks steerable NPC interactions without any domain-specific retraining. Evaluated on two Street Fighter games, ReactiveGWM maintains fine-grain player controllability while achieving robust, prompt-aligned NPC strategy adherence, paving the way for scalable, strategy-rich interaction with the NPC.