iVideoGPT: Interactive VideoGPTs are Scalable World Models

TL;DR

iVideoGPT introduces a scalable transformer-based framework that integrates multimodal signals for interactive video prediction and decision-making, enabling versatile applications in reinforcement learning and planning.

Contribution

The paper presents a novel compressive tokenization method and a scalable autoregressive transformer architecture for interactive world models trained on extensive manipulation data.

Findings

Achieves competitive performance in video prediction and planning tasks

Successfully pre-trained on millions of manipulation trajectories

Bridges generative video models with practical reinforcement learning applications

Abstract

World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. However, the high demand for interactivity poses challenges in harnessing recent advancements in video generative models for developing world models at scale. This work introduces Interactive VideoGPT (iVideoGPT), a scalable autoregressive transformer framework that integrates multimodal signals--visual observations, actions, and rewards--into a sequence of tokens, facilitating an interactive experience of agents via next-token prediction. iVideoGPT features a novel compressive tokenization technique that efficiently discretizes high-dimensional visual observations. Leveraging its scalable architecture, we are able to pre-train iVideoGPT on millions of human and robotic manipulation trajectories, establishing a versatile foundation that is adaptable to serve as interactive world models for a wide range of downstream tasks. These include action-conditioned video prediction, visual planning, and model-based reinforcement learning, where iVideoGPT achieves competitive performance compared with state-of-the-art methods. Our work advances the development of interactive general world models, bridging the gap between generative video models and practical model-based reinforcement learning applications. Code and pre-trained models are available at https://thuml.github.io/iVideoGPT.