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R1 V
Overview :
R1-V is a project focused on enhancing the generalization capabilities of visual language models (VLMs). Using verified reward reinforcement learning (RLVR) technology, it significantly improves the generalization abilities of VLMs in visual counting tasks, particularly excelling in out-of-distribution (OOD) tests. The significance of this technology lies in its ability to efficiently optimize large-scale models at an extremely low cost (training costs as low as $2.62), offering new insights into the practical applications of visual language models. The project is based on improvements to existing VLM training methods, aiming to enhance model performance in complex visual tasks through innovative training strategies. Its open-source nature also makes it a vital resource for researchers and developers exploring and applying advanced VLM technologies.
Target Users :
This product is designed for researchers, developers, and enterprises who need efficient training and optimization of visual language models, especially teams looking to achieve performance breakthroughs with limited resources. The low cost and high efficiency of R1-V make it an ideal choice for exploring the generalization capabilities of visual language models, helping users quickly validate and deploy advanced VLM technologies.
Use Cases
Researchers can utilize the R1-V technology framework to explore new training strategies for visual language models, improving their performance in complex visual tasks.
Developers can quickly build and optimize their visual language applications, such as intelligent image recognition systems, based on R1-V's open-source code and models.
Enterprises can leverage R1-V's low-cost training solutions to achieve rapid deployment and application of visual language models within a limited budget, enhancing business efficiency.
Features
Utilizes RLVR technology, outperforming traditional CoT-SFT methods to enhance model generalization.
In just 100 training steps, the 2B model can surpass the performance of the 72B model in OOD tests.
Training on 8 A100 GPUs for just 30 minutes can cost as low as $2.62.
Provides complete open-source code, models, and datasets for ease of research and application.
Supports various training configurations to accommodate different hardware environments and needs.
How to Use
1. Clone the project repository to your local machine.
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2. Install the required Python packages.
3. Set environment variables such as DEBUG_MODE and LOG_PATH.
4. Start the training script using the torchrun command, specifying parameters such as output directory, model path, and dataset path.
5. Monitor the training process by reviewing log files for training progress and results.
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