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Sequoia Capital Cast

02/25/25

@ Sequoia Capital

High-quality training data is crucial for the success of Deep Research, as the quality of the input data significantly influences the model's output quality.

Video

OpenAI’s Deep Research Team on Why Reinforcement Learning is the Future for AI Agents

@ Sequoia Capital

OpenAI’s Deep Research Team on Why Reinforcement Learning is the Future for AI Agents

02/25/25

Related Takeaways

Sequoia Capital Cast

02/25/25

@ Sequoia Capital

The quality difference among Deep Research products is evident, stemming from the effort put into constructing the datasets and the optimization capabilities of the O Series models.

Jeff Dean

08/08/17

@ Y Combinator

For supervised tasks with clearly defined inputs and outputs, having sufficient training data is crucial for success, and the amount of computational power required varies by task.

Daniel Kokotajlo

04/04/25

@ Dwarkesh Patel

The quality of researchers, their ability to learn from data, and the efficiency of experiments are crucial inputs to AI R&D progress.

Isa Fulford

05/08/25

@ Sequoia Capital

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.

Demis Hassabis

05/23/25

@ Google for Developers

Synthetic data can be used in training models, but it must be created from the same distribution to maintain quality.

Carl Eschenbach

05/06/25

@ Sequoia Capital

Data is the new user interface for AI, and having highly curated data is crucial for achieving great outcomes with AI agents.

Fei-Fei Li

09/21/24

@ a16z

The success of deep learning is not just about algorithms; it's also about the availability of large datasets, like ImageNet, which have driven advancements in the field.

a16z Cast

02/07/25

@ a16z

The release of DeepSeek's reasoning traces is significant as it allows for better training of smaller models quickly and cheaply.

Sequoia Capital Cast

02/25/25

@ Sequoia Capital

Personalized education is a promising use case for Deep Research, as it can compile information on topics users want to learn about, creating tailored reports to aid in their understanding.