DeepSeek-R1 reasoning model
Here is the translation:
“DeepSeek’s Janus Pro is a multimodal understanding model that can handle both visual and linguistic information simultaneously, showing fine details and accurate semantic understanding. However, its performance in tasks such as OCR at finer granularity is limited by input resolution, which may affect image generation and vision-based segmentation.
Janus Pro demonstrates through innovative architecture design and decoupled training strategies that the two tasks of “understanding” and “generation” can be optimized within a unified framework. Its design seems to learn from the human brain, with the visual understanding encoder similar to left-brain analytical functions and the image generation encoder similar to right-brain artistic creative ability.
DeepSeek also released a multimodal understanding model called JanusFlow-1.3B, which directly combines the understanding framework based on visual encoders and large language models with the generation framework based on corrected flow Rectified Flow. JanusFlow can be trained end-to-end in a single large language model, significantly improving the performance of unified models.
JanusFlow adopts two key strategies: decoupled encoding for understanding and generation, as well as aligning their representations during joint training. These two strategies together promote efficient training and outstanding performance of JanusFlow.
In summary, DeepSeek has once again driven the development of multimodal unified models, providing new architecture design, training strategies, and computational optimization. Although there are currently some limitations, it is believed that these problems will be resolved as technology continues to advance.”
Translation
DeepSeek的Janus Pro是一个多模态理解模型，它能够同时处理视觉和语言信息，展示了细致的细节和准确的语义理解。然而，它在OCR等细粒度任务中的表现受到输入分辨率的限制，这可能会影响图像生成和视觉分词器的性能。
Janus Pro通过创新的架构设计和解耦的训练策略，证明了“理解”和“生成”的两个任务可以在一个统一框架下达到最优状态。它的设计似乎是向人脑学习的，因为图像理解编码器类似左脑的分析功能，而图像生成编码器则类似右脑的艺术创造能力。
DeepSeek还发布了一个多模态理解模型JanusFlow-1.3B，它将基于视觉编码器和大语言模型的理解框架与基于校正流Rectified Flow的生成框架直接融合。JanusFlow能够在单一大语言模型中进行端到端训练，显著提高统一模型的性能。
JanusFlow采用了两种关键策略：解耦理解和生成编码器，以及在统一训练期间对齐它们的表征。这两个策略共同促进了JanusFlow的高效训练并取得了优异的成绩。
总之，DeepSeek再一次推动了多模态大一统模型的发展，并提供了新颖的架构设计、训练策略和算力优化。虽然当前存在一些局限性，但相信随着技术的不断进步，这些问题将会得到解决。
Reference:
https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf