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OpenAI’s Deep Research Team on Why Reinforcement Learning is the Future for AI Agents
@ Sequoia Capital
02/25/25
Takeaways
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
The key lesson in machine learning is that you get what you optimize for; setting up a system to directly optimize for desired outcomes yields significantly better results than trying to combine unoptimized models.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Reinforcement learning on top of models will be critical for building the most powerful AI agents, as it allows for better adaptability and performance in complex tasks.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Deep Research is an AI agent that can search multiple online sources to create comprehensive reports, completing tasks that would take humans hours in just 5 to 30 minutes, providing detailed answers with specific sources.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
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.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
The primary users of Deep Research include professionals engaged in knowledge work, such as market analysis, scientific research, and medical inquiries, as well as individuals seeking assistance with personal projects like travel planning.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Deep Research has been surprisingly popular for coding tasks, helping users with code searches, documentation, and script writing, which was not initially anticipated as a primary use case. It also excels at synthesizing information and finding specific, hard-to-locate facts on the internet, especially when given detailed descriptions of what users want.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Deep Research excels in providing detailed comparisons and reviews for products, making it a valuable tool for consumers looking to make informed purchasing decisions without spending excessive time on research.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
The model is effective at following complex instructions, allowing users to request multiple types of information in one query, such as product details, comparisons, and reviews from various sources.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Deep Research can embed images and create graphs in its responses, enhancing the quality of information provided, although these features are not yet available for consumer use.
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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.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Deep Research is particularly effective for detailed queries that require extensive reading and understanding of web content to provide accurate answers.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
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.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Deep Research's ability to train end-to-end allows it to adapt flexibly to live web information, making it capable of creative searches and generating insightful responses.
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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.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Citations are included in Deep Research outputs, allowing users to verify the sources of information, which is essential for building trust in the model's responses.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
The ultimate goal for future AI agents is to seamlessly integrate various tasks, such as web searching and computer operations, in a more natural and human-like manner.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
The clarification flow in Deep Research allows the model to ask users for more information, enhancing the interaction and improving the accuracy of the responses. To get the best response from the research model, the prompt needs to be well-specified and detailed, as users typically don't provide all necessary information in the first prompt.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Users are finding their own workflows to enhance their prompts before sending them to Deep Research, indicating a growing adaptability in how they interact with the model.
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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.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Deep Research is designed to save users significant time, potentially giving back 10% to 25% of their work time, depending on the type of tasks they perform.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Deep Research is not about replacing jobs but enhancing knowledge work, allowing professionals to spend less time on information gathering and more on decision-making.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
In the medical field, Deep Research can help doctors quickly find relevant literature and clinical trials, saving them time and improving patient care.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
The impact of Deep Research is profound, enabling tasks that previously took hours to be completed in minutes, thus allowing users to engage in more complex or numerous tasks.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Education can be transformed by AI systems that personalize learning, making it more efficient and engaging compared to traditional methods like reading textbooks.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
The year 2025 is anticipated to be significant for AI agents, with a focus on their breakout potential in various applications.
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Sequoia Capital Cast
02/25/25
@ Sequoia Capital
Reinforcement learning is experiencing a resurgence due to advancements in language models, making it ripe for tuning to specific use cases with defined reward functions.