JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse (2503.16365v1)

Abstract: Recently, action-based decision-making in open-world environments has gained significant attention. Visual Language Action (VLA) models, pretrained on large-scale web datasets, have shown promise in decision-making tasks. However, previous work has primarily focused on action post-training, often neglecting enhancements to the foundational model itself. In response, we introduce a novel approach, Act from Visual Language Post-Training, which refines Visual LLMs (VLMs) through visual and linguistic guidance in a self-supervised manner. This enhancement improves the models' capabilities in world knowledge, visual recognition, and spatial grounding in open-world environments. Following the above post-training paradigms, we obtain the first VLA models in Minecraft that can follow human instructions on over 1k different atomic tasks, including crafting, smelting, cooking, mining, and killing. Our experiments demonstrate that post-training on non-trajectory tasks leads to a significant 40% improvement over the best agent baseline on a diverse set of atomic tasks. Furthermore, we demonstrate that our approach surpasses traditional imitation learning-based policies in Minecraft, achieving state-of-the-art performance. We have open-sourced the code, models, and datasets to foster further research. The project page can be found in https://craftjarvis.github.io/JarvisVLA.

Analysis of JARVIS-VLA: Enhancing Vision-LLMs for Visual Games

The paper in question presents JARVIS-VLA, a sophisticated approach aimed at improving Large-Scale Vision LLMs' (VLMs) ability in playing visual games such as Minecraft using post-training refinements. The core objective of this research is to elevate the underlying capabilities of VLMs, which have typically been concentrated on action post-training, by innovatively focusing on visual and linguistic guidance.

Background and Motivation

The exploration of decision-making in open-world environments has become increasingly popular, with VLMs showing potential in addressing these complex tasks. These models have been pretrained on vast datasets sourced from the internet, analogous to how models like GPT and LLAMA have excelled in language tasks. However, the application of such models to dynamic environments like Minecraft presents unique challenges, primarily due to their reliance on imitation learning, which often limits their flexibility and generalizability.

Methodology

The JARVIS-VLA approach introduces Visual Language Post-Training, designed to enhance the innate abilities of VLMs by incorporating visual and linguistic guidance in a self-supervised manner. The method is structured into three significant phases:

1. Post-Training LLMs: This stage focuses on refining the language aspects of the VLM using extensive textual datasets pertinent to the gaming environments, which in this case, is Minecraft.
2. Post-Training Vision Encoder and LLMs: In this phase, both the vision encoder and LLMs are honed using multimodal datasets, integrating visual language alignment and spatial grounding tasks. This dual refinement aims to augment the model's ability to synergize vision and linguistic tasks seamlessly.
3. Imitation Learning on Trajectories: Finally, the model is trained on gameplay data to imitate expert actions, with a novel emphasis on reducing the reliance on massive trajectory datasets traditionally necessary for training.

Experimental Results

Empirical evaluations reveal significant improvements over existing methodologies, with JARVIS-VLA achieving a 40% performance boost on various atomic tasks in Minecraft compared to baseline agents. The approach also surpasses standard imitation learning techniques, showcasing superior performance across diverse tasks such as crafting, smelting, and mining. Post-training using rich, yet non-trajectory-based datasets allowed the model to generalize effectively to new scenarios within the game environment.

Implications and Future Directions

The implications of this research are multifaceted, featuring both practical and theoretical advancements. Practically, this novel training paradigm proposes a new method to create more adaptable and efficient decision-making models in open-world settings. Theoretically, it provides insights into the integration of linguistic and visual modalities, potentially influencing future developments in AI where autonomous agents need to understand and operate in visually rich environments.

The research opens the door for further studies that can expand this framework's applicability to other domains beyond gaming. Future developments may explore incorporating more advanced multimodal datasets and the application of this training framework for other complex tasks requiring intricate visual-linguistic integration.

To summarize, the paper introduces a compelling paradigm shift in training VLMs for visual game play by innovating past conventional imitation learning and trajectory-based training approaches, promising a leap forward in developing computational agents that excel in real-world, dynamic environments.