To develop Deep Research, we created reinforcement learning tasks to teach the model browsing and data analysis capabilities, and provided it access to tools for training.

We're excited to continue developing deep research, improving its reliability, and integrating it into our main reasoning models.

Through thousands of reinforcement learning steps, DeepSeek's model learned advanced reasoning skills, including extended chains of thought and the ability to recognize and correct its own mistakes.

The model that powers Deep Research is a fine-tuned version of the O3 model, specifically trained on challenging browsing and reasoning tasks, enhancing its online search and analysis capabilities.

Deep Research was developed to tackle tasks requiring extensive online research and reasoning, enabling users to efficiently gather information and make informed decisions across various domains.

The DeepSeek paper detailed that they used reasoning-heavy but standard post-training techniques after large-scale reasoning training.

Deep reinforcement learning, which DeepMind is known for, allows systems to learn from scratch by trial and error, achieving superhuman performance in some tasks.

The reasoning models being developed by DeepSeek are designed to utilize more compute, emphasizing the growing importance of inference in complex AI tasks.

DeepSeek's R1 model is one of the first large models to achieve top-tier results purely through reinforcement learning, marking a significant milestone in AI development. Additionally, DeepSeek introduced a cold start phase for fine-tuning on structured reasoning examples before reinforcement learning, which eliminated language mixing issues and made outputs far more comprehensible.