Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone
Dream-VL and Dream-VLA leverage diffusion language models for breakthroughs in vision-language and vision-language-action tasks.
Key Findings
Methodology
The study introduces Dream-VL, a vision-language model, and Dream-VLA, a vision-language-action model, both based on diffusion language models. Dream-VL is trained on multimodal data, excelling in visual understanding and planning tasks. Dream-VLA, through continuous pre-training on open robotic datasets, shows superior performance in action generation tasks.
Key Results
- Dream-VLA achieved a 97.2% success rate on the LIBERO dataset and 71.4% on SimplerEnv-Bridge, surpassing leading models like π0 and GR00T-N1.
- In visual planning tasks, Dream-VL excels on benchmarks like LIBERO and ViPlan, demonstrating potential in long-horizon planning tasks.
- Experiments show Dream-VL outperforms existing diffusion models on multidisciplinary knowledge and mathematical reasoning benchmarks.
Significance
By introducing diffusion models as the foundation for vision-language and vision-language-action tasks, this study addresses the bottleneck of autoregressive models in long-horizon planning and global reasoning. This approach is significant not only academically but also offers new insights for practical applications in robotic control and multimodal interaction.
Technical Contribution
Technically, Dream-VL and Dream-VLA achieve rich fusion of visual and text features through bidirectional attention mechanisms, enhancing visual planning capabilities and supporting action chunking and parallel generation, significantly accelerating convergence in downstream fine-tuning.
Novelty
This study is the first to apply diffusion language models to vision-language and vision-language-action tasks, overcoming the limitations of autoregressive models and providing more efficient global reasoning capabilities.
Limitations
- Despite excellent performance on multiple benchmarks, the model's performance in complex visual scenarios still needs improvement.
- The computational cost is high when handling large-scale data.
- The model's generalization ability in specific tasks requires further validation.
Future Work
Future research directions include exploring larger datasets to improve model generalization and optimizing computational efficiency to reduce resource consumption.
AI Executive Summary
Current autoregressive vision-language models face limitations in complex visual planning and dynamic robotic control. Dream-VL and Dream-VLA offer a novel solution by introducing diffusion language models. The bidirectional nature of diffusion models excels in visual planning and action generation tasks, especially in long-horizon planning and global reasoning. Experimental results show Dream-VLA surpasses existing leading models on benchmarks like LIBERO and SimplerEnv, demonstrating potential in robotic control. However, the computational cost is high when handling large-scale data, and future research will focus on improving computational efficiency and generalization capabilities.
Deep Analysis
Background
With the advancement of AI technology, vision-language models play an increasingly important role in fields like medical diagnosis, scientific discovery, and autonomous driving. Traditional autoregressive models have made significant progress in multimodal dialogue and scene understanding but face bottlenecks in long-horizon planning and global reasoning. Diffusion models, with their advantages in language modeling, offer a potential solution to this problem.
Core Problem
Autoregressive vision-language models perform poorly in complex visual planning and dynamic robotic control, mainly due to their sequential generation nature leading to error accumulation during inference, limiting global reasoning capabilities. This issue is particularly prominent in tasks requiring long-horizon planning and global reasoning.
Innovation
Dream-VL and Dream-VLA introduce diffusion language models to overcome the limitations of autoregressive models. The bidirectional nature of diffusion models supports more efficient global reasoning and parallel generation, significantly improving performance in visual planning and action generation tasks.
Methodology
- �� Dream-VL is trained on multimodal data, enhancing visual understanding and planning capabilities.
- �� Dream-VLA is continuously pre-trained on open robotic datasets, improving performance in action generation tasks.
- �� Bidirectional attention mechanisms achieve rich fusion of visual and text features.
Experiments
The experimental design includes evaluations on benchmarks like LIBERO and SimplerEnv, using multimodal data for training and testing. Key hyperparameters include the model's learning rate and batch size. Results show Dream-VL and Dream-VLA surpass existing leading models on multiple benchmarks.
Results
Results show Dream-VLA achieved a 97.2% success rate on the LIBERO dataset, significantly surpassing leading models like π0 and GR00T-N1. Dream-VL outperforms existing diffusion models on multidisciplinary knowledge and mathematical reasoning benchmarks, demonstrating potential in long-horizon planning tasks.
Applications
Dream-VL and Dream-VLA have broad application potential in fields like robotic control, visual planning, and multimodal interaction. Their bidirectional nature and parallel generation capabilities excel in tasks requiring long-horizon planning and global reasoning.
Limitations & Outlook
Despite excellent performance on multiple benchmarks, the model's performance in complex visual scenarios still needs improvement. Additionally, the computational cost is high when handling large-scale data, and future research will focus on improving computational efficiency and generalization capabilities.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. An autoregressive model is like a chef who follows a recipe step by step, while a diffusion model is like a chef who can prepare multiple dishes simultaneously. The diffusion model can consider multiple steps at once, avoiding error accumulation. This ability makes diffusion models perform better in tasks requiring long-horizon planning, like preparing multiple dishes in the kitchen.
ELI14 Explained like you're 14
Imagine you're playing a game like Minecraft that requires planning. An autoregressive model is like placing blocks one by one, while a diffusion model can consider multiple steps at once, like building multiple structures simultaneously. This makes diffusion models perform better in tasks requiring long-horizon planning, like building multiple structures in the game.
Glossary
Diffusion Model
A model that generates data by iteratively denoising, suitable for tasks requiring global reasoning.
Used as the foundation for vision-language and vision-language-action tasks in this paper.
Autoregressive Model
A model that generates data sequentially, suitable for short-term reasoning tasks.
Used as a comparison to show its limitations in long-horizon planning.
Vision-Language Model
A model that combines visual and language information for reasoning, widely used in multimodal tasks.
Used for visual understanding and planning tasks in this paper.
Vision-Language-Action Model
A model that combines visual, language, and action information for reasoning, suitable for robotic control tasks.
Used for action generation tasks in this paper.
Bidirectional Attention
A mechanism that considers information from both directions, enhancing information fusion capability.
Used to enhance the fusion of visual and text features in this paper.
Open Questions Unanswered questions from this research
- 1 How to improve model generalization without increasing computational cost?
- 2 How to further improve model performance in more complex visual scenarios?
Applications
Immediate Applications
Robotic Control
Dream-VLA can enhance action generation capabilities in complex tasks, applicable to industrial and home robots.
Long-term Vision
Multimodal Interaction
Diffusion models have great potential in multimodal interaction, potentially transforming human-computer interaction in the future.
Abstract
While autoregressive Large Vision-Language Models (VLMs) have achieved remarkable success, their sequential generation often limits their efficacy in complex visual planning and dynamic robotic control. In this work, we investigate the potential of constructing Vision-Language Models upon diffusion-based large language models (dLLMs) to overcome these limitations. We introduce Dream-VL, an open diffusion-based VLM (dVLM) that achieves state-of-the-art performance among previous dVLMs. Dream-VL is comparable to top-tier AR-based VLMs trained on open data on various benchmarks but exhibits superior potential when applied to visual planning tasks. Building upon Dream-VL, we introduce Dream-VLA, a dLLM-based Vision-Language-Action model (dVLA) developed through continuous pre-training on open robotic datasets. We demonstrate that the natively bidirectional nature of this diffusion backbone serves as a superior foundation for VLA tasks, inherently suited for action chunking and parallel generation, leading to significantly faster convergence in downstream fine-tuning. Dream-VLA achieves top-tier performance of 97.2% average success rate on LIBERO, 71.4% overall average on SimplerEnv-Bridge, and 60.5% overall average on SimplerEnv-Fractal, surpassing leading models such as $π_0$ and GR00T-N1. We also validate that dVLMs surpass AR baselines on downstream tasks across different training objectives. We release both Dream-VL and Dream-VLA to facilitate further research in the community.