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| # AceAction Research | |
| **Learning to Act in Interactive Worlds.** | |
| AceAction is a research initiative exploring how intelligent agents understand goals, perceive dynamic environments, and translate intentions into actions. Our interests span **vision-language-action (VLA) learning**, **action-conditioned world modeling**, and **long-horizon decision-making** in interactive 3D worlds. | |
| ## 📄 Papers | |
| ### [AceAction-5M](https://arxiv.org/abs/2611.04241) · arXiv 2026 | |
| **Hierarchical Goals for Generalist Action Learning Across 3D Game Worlds** | |
| A multi-game action-learning dataset with hierarchically structured goals, subgoals, and skills across **42 games** and over **5 million annotated episodes**. | |
| ### [3A-VLA](https://arxiv.org/abs/2602.02212) · NeurIPS 2026 | |
| **Abstraction-Aligned Action Learning for Vision-Language Agents in 3D Game Worlds** | |