VideoWorld: Exploring Knowledge Learning from Unlabeled Videos

Abstract: This work explores whether a deep generative model can learn complex knowledge solely from visual input, in contrast to the prevalent focus on text-based models like LLMs. We develop VideoWorld, an auto-regressive video generation model trained on unlabeled video data, and test its knowledge acquisition abilities in video-based Go and robotic control tasks. Our experiments reveal two key findings: (1) video-only training provides sufficient information for learning knowledge, including rules, reasoning and planning capabilities, and (2) the representation of visual change is crucial for knowledge acquisition. To improve both the efficiency and efficacy of this process, we introduce the Latent Dynamics Model (LDM) as a key component of VideoWorld. Remarkably, VideoWorld reaches a 5-dan professional level in the Video-GoBench with just a 300-million-parameter model, without relying on search algorithms or reward mechanisms typical in reinforcement learning. In robotic tasks, VideoWorld effectively learns diverse control operations and generalizes across environments, approaching the performance of oracle models in CALVIN and RLBench. This study opens new avenues for knowledge acquisition from visual data, with all code, data, and models open-sourced for further research.

• The paper introduces VideoWorld, an auto-regressive video generation framework that learns complex knowledge like game rules and control operations solely from unlabeled visual data using a generative model and latent dynamics representation.
• Evaluating on Go, VideoWorld achieved a 5-dan professional skill level on Video-GoBench using only visual learning, and showed strong performance in robotic tasks like CALVIN and RLBench.
• The research suggests AI can learn sophisticated knowledge by directly processing visual data, potentially reducing reliance on text and paving the way for models that learn more like biological systems.

Overview of "VideoWorld: Exploring Knowledge Learning from Unlabeled Videos"

The paper introduces VideoWorld, a pioneering effort to explore knowledge acquisition from purely visual data using an auto-regressive video generation framework. This study diverges from the conventional focus on text-based learning models, such as LLMs, and seeks to investigate whether deep generative models can gain complex knowledge solely from visual input. VideoWorld is designed to autonomously learn rules, reasoning, and planning within video-based environments, notably in Go and robotic tasks.

Methodology

VideoWorld employs an efficient architecture that integrates a deep generative model for video sequence generation. The architecture includes a VideoQuantum Variational Autoencoder (VQ-VAE) for encoding video frames into discrete tokens and a transformer-based model to predict subsequent video frames based on prior frames, following an auto-regressive paradigm. A key feature introduced in this work is the Latent Dynamics Model (LDM), which aims to improve the efficiency of visual learning by providing a compact representation of multi-step visual changes, crucial for understanding temporal dynamics central to task execution.

Key Findings

The empirical results from the paper underscore two major findings:

1. Knowledge Learning from Visual Data: The generative models, when trained solely on video data, demonstrate the ability to learn sophisticated knowledge, including game rules and control operations. In the Go domain, VideoWorld achieves a 5-dan professional skill level on the Video-GoBench metric without traditional reinforcement learning techniques, such as search-based strategies or reward mechanisms.
2. Importance of Visual Change Representation: The representation of visual change significantly influences learning efficacy. The use of latent codes in capturing future visual changes enhances both the learning process and the ability of the model to grasp intricate knowledge from static visual content.

Evaluation and Results

VideoWorld was evaluated against both Go and robotic benchmarks. In Go, VideoWorld's playing ability was compared to various levels of the KataGo engine, establishing that VideoWorld could reach a substantial level of competency (equivalent to 5-dan players) using 300 million parameters. The legal rate of move generation and strategic depth, as assessed by action-value scores comparable to oracle levels, validate its efficiency in learning and application.

Furthermore, VideoWorld's effectiveness extends to robotic tasks, where it demonstrated promising outcomes in environments such as CALVIN and RLBench. The model achieved successful task execution rates nearing oracle performance, indicating its proficiency in learning control operations and adapting across different environments.

Implications and Future Directions

The implications of this research are notable for advancing AI's understanding of acquiring knowledge from visual data, as opposed to text, which may be limited in capturing dynamic and real-world information. The effectiveness of VideoWorld indicates a potential shift in developing AI models that are less reliant on linguistic data, aiming instead at how organic beings learn from their surroundings.

Future research directions could involve scaling VideoWorld with improved visual representation techniques and extensive pretraining across diverse visual datasets, potentially broadening its application in more complex, real-world scenarios. Moreover, integrating this visual learning approach with multimodal methods could enhance AI's ability to simultaneously process and understand complex visual and textual information.

Overall, VideoWorld represents a promising advancement in autonomous learning from video data, offering a novel perspective on how AI might continue to evolve towards more generalized knowledge representation systems.