Recently, Tencent Hunchuan officially released a new research achievement in collaboration with Shanghai AI Lab, Fudan University, and Shanghai Creative Intelligence Academy — the Unified Reward-Think (URT) model. This innovative model not only possesses strong long-chain reasoning capabilities but also achieves "thinking" ability in visual tasks for the first time. As a result, reward models can more accurately evaluate complex visual generation and understanding tasks.
The release of the Unified Multi-modal Reward Model marks a new height in the application of reward models in various visual tasks. In the past, many visual tasks often faced issues such as inaccurate evaluation and insufficient reasoning ability. This new model was developed precisely to overcome these limitations. By using deep learning and multi-modal fusion technology, the model can generalize and reason across multiple visual tasks, enhancing interpretability. This means that when performing tasks like image generation and image understanding, the model can consider various factors more comprehensively and make more reasonable judgments.
Image source note: Image generated by AI, licensed by Midjourney.
The project's open-source nature allows researchers to freely use this model and provides a broader platform for research within the entire AI community. Tencent Hunchuan stated that the open-source content includes the model, dataset, training scripts, and evaluation tools, which will help promote progress and innovation in relevant fields. Researchers and developers can base their work on this model to explore more application scenarios.
In addition, this move by Tencent Hunchuan reflects its continuous innovation and open attitude in the field of artificial intelligence. Globally, the rapid development of artificial intelligence technology has prompted major tech companies to increase their R&D efforts and introduce more forward-looking technologies and applications. Tencent Hunchuan's open-sourced Unified Multi-modal Reward Model is a microcosm of this trend.
With the release and open-sourcing of this model, we will see more possibilities and application prospects in multi-modal AI research and visual task evaluations in the future.