OpenAI’s ‘Strawberry’ Project: Enhancing AI Reasoning

OpenAI's 'Strawberry' project focuses on advancing AI reasoning, planning, and execution. Supported by Microsoft, this initiative aims to enable AI models to perform autonomous deep research, handle complex tasks, and bridge the gap between current capabilities and human-like reasoning.
OpenAI's 'Strawberry' Project: Enhancing AI Reasoning

OpenAI, the company behind ChatGPT, is developing a new approach to artificial intelligence under the code name “Strawberry.” According to internal documentation and a source familiar with the project, Strawberry aims to enhance the reasoning capabilities of AI models, a feature that has been elusive for current AI technologies.

What is OpenAI’s Strawberry Project?


The details of the Strawberry project, previously unreleased, indicate that OpenAI is focusing on advanced reasoning within its AI models. This initiative, supported by Microsoft, is critical as the company seeks to demonstrate that its AI models can handle complex reasoning tasks.

Internal documents, reviewed by Reuters, reveal that OpenAI has been working on Strawberry since at least May. However, the precise details and timeline of the project’s completion remain confidential. The project’s inner workings are closely guarded, even within OpenAI.

Strawberry Project Goals and Objectives

Strawberry’s primary objective is to enable AI models to autonomously navigate the internet and perform what OpenAI terms “deep research.” This capability involves not just generating answers but planning and executing actions reliably over extended periods. Achieving such a level of reasoning would be a significant advancement, overcoming current AI limitations like logical fallacies and task-specific errors.

Enhancing AI’s Planning and Execution with Strawberry

Current AI models excel at generating responses and performing specific tasks but struggle with long-term planning and execution. Strawberry aims to bridge this gap by developing models that can foresee, plan, and execute a series of actions to achieve a goal. This includes the ability to understand and navigate the complexities of the internet to gather information and perform tasks autonomously.

Enabling AI Autonomous Deep Research with Strawberry

One of the most ambitious goals of Strawberry is to enable AI to conduct “deep research.” This involves navigating the web, gathering data, and synthesizing information in a way that mimics human researchers. By autonomously performing these tasks, Strawberry aims to elevate the capabilities of AI from mere assistants to independent researchers.

OpenAI’s Vision: Human-Like AI Reasoning

OpenAI’s spokesperson highlighted the company’s goal for AI to understand the world more similarly to humans. They emphasized that continuous research into AI capabilities is a standard industry practice aimed at improving reasoning over time. However, the spokesperson did not directly address questions about the specifics of the Strawberry project.

From Q* to Strawberry: Evolution of AI Reasoning

Strawberry evolved from an earlier project known as Q*, which was internally considered a breakthrough last year. Demonstrations of Q* showcased the ability to solve complex science and math problems beyond the reach of current commercial AI models. This predecessor laid the groundwork for Strawberry’s advanced reasoning capabilities.

Q* Demonstrations and Implications

Earlier this year, Q* was demonstrated to have the capability to tackle intricate science and mathematics problems, which current commercially available AI models struggle with. These capabilities highlighted the potential of Strawberry to handle complex reasoning tasks and set the stage for its development.

Achieving Human-Like Reasoning in AI

At a recent internal meeting, OpenAI demonstrated a research project with purported human-like reasoning skills, although it is unclear if this was related to Strawberry. OpenAI’s CEO, Sam Altman, has emphasized that advancements in AI reasoning are crucial for the technology’s future.

The Importance of Reasoning in AI Advancement

The ability to reason like humans is seen as a critical advancement for AI. While AI models can already process and generate text quickly, they often falter when faced with problems requiring common sense or logical reasoning. OpenAI aims to address these shortcomings with Strawberry, moving AI closer to human-like intelligence.

Industry Efforts to Enhance AI Reasoning

OpenAI is not alone in this endeavor. Companies like Google, Meta, and Microsoft, along with various academic labs, are experimenting with techniques to enhance AI reasoning. While some researchers, like Meta’s Yann LeCun, remain skeptical about the capabilities of large language models (LLMs) in achieving human-like reasoning, OpenAI’s Strawberry project aims to address these challenges.

Competitive AI Reasoning Developments

The race to improve AI reasoning capabilities is heating up across the tech industry. Google, Meta, and Microsoft are also exploring different techniques to enhance their AI models. This competitive landscape drives innovation and pushes the boundaries of what AI can achieve.

Strawberry’s Technical Approach

Strawberry employs a specialized post-training process to refine AI models after their initial training on large datasets. This approach, similar to the “Self-Taught Reasoner” (STaR) method developed at Stanford, involves iterative self-training to boost intelligence levels. STaR’s potential to elevate AI beyond human-level intelligence is both exciting and concerning, according to its creator, Stanford professor Noah Goodman.

Post-Training and Fine-Tuning in Strawberry Project

The post-training phase involves fine-tuning AI models to enhance their performance in specific ways. This includes human feedback and providing examples of good and bad responses. Strawberry’s approach to post-training aims to refine models to handle complex reasoning tasks more effectively.

Focusing on Long-Horizon Tasks in AI

One of Strawberry’s key focuses is on long-horizon tasks (LHT), which require extensive planning and a series of actions over time. To achieve this, OpenAI is developing and testing models on a “deep-research” dataset, although specific details about the dataset remain undisclosed.

Managing Complex Tasks with AI

Long-horizon tasks require AI to plan and execute actions over extended periods, something current models struggle with. Strawberry aims to enable AI to manage these complex tasks, enhancing their utility in various applications, from scientific research to software development.

Enhancing AI with Autonomous Research Abilities

OpenAI aims for its AI models to autonomously conduct research by browsing the web with the help of a “computer-using agent” (CUA). This agent can take actions based on its findings, significantly advancing the capabilities of AI in performing tasks traditionally done by software and machine learning engineers.

Using Computer-Using Agents for AI Deep Research

The integration of CUAs allows AI models to autonomously gather and process information, performing tasks that require long-term planning and execution. This capability is crucial for applications that require extensive research and decision-making.

Conclusion: Implications of OpenAI’s Strawberry Project

OpenAI’s Strawberry project represents a significant leap forward in AI reasoning capabilities. By enabling AI models to plan, execute complex tasks, and conduct autonomous research, OpenAI aims to push the boundaries of what artificial intelligence can achieve. As the project progresses, it will be crucial to monitor its development and the broader implications for AI technology.


Recent Content

Predicting AI’s future is difficult, but its impact on work and life is certain. Many organizations are hesitant, “nibbling around the corners” instead of embracing transformative applications. This slow adoption, however, has allowed us to better understand and utilize large language models. The AI revolution mirrors the steam engine transformation, with organizations needing to integrate AI to stay competitive. The biggest winners will be those that successfully integrate AI, gaining a significant advantage. The most significant transformation will be in knowledge management, how organizations make decisions and leverage collective intelligence.
Connected aviation is transforming airports with secure private networks, IoT, and real-time data. This article unpacks how smart airports boost efficiency, safety, and passenger experience while unlocking new business value with real-world case studies from Heathrow, Changi, Dubai, and more.
Connected aviation is reshaping airports into smart, seamless ecosystems inside and outside the terminal. This case study reveals how hubs like Changi, Schiphol, and SAN use private networks, IoT, and cross-team collaboration to improve passenger flow, airside operations, sustainability, and safety.
Connected aviation is reshaping airports with autonomous systems, from security drones to robotic baggage vehicles and self-driving tugs. Automation improves safety, cuts turnaround times, and delivers a smoother passenger experience. Learn how airports use AI and robotics to stay competitive.
IoT and digital transformation are reshaping aviation by turning traditional airports into smart, data-driven ecosystems. From predictive maintenance to autonomous vehicles and real-time passenger insights, connected sensors and AI power safer, more efficient, and sustainable airport operations.
Whitepaper
Telecom networks are facing unprecedented complexity with 5G, IoT, and cloud services. Traditional service assurance methods are becoming obsolete, making AI-driven, real-time analytics essential for competitive advantage. This independent industry whitepaper explores how DPUs, GPUs, and Generative AI (GenAI) are enabling predictive automation, reducing operational costs, and improving service quality....
Whitepaper
Explore the collaboration between Purdue Research Foundation, Purdue University, Ericsson, and Saab at the Aviation Innovation Hub. Discover how private 5G networks, real-time analytics, and sustainable innovations are shaping the "Airport of the Future" for a smarter, safer, and greener aviation industry....
Article & Insights
This article explores the deployment of 5G NR Transparent Non-Terrestrial Networks (NTNs), detailing the architecture's advantages and challenges. It highlights how this "bent-pipe" NTN approach integrates ground-based gNodeB components with NGSO satellite constellations to expand global connectivity. Key challenges like moving beam management, interference mitigation, and latency are discussed, underscoring...

Download Magazine

With Subscription

Subscribe To Our Newsletter

Scroll to Top