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

In Technology Game Changers, leaders from Agility Robotics, Lenovo, Databricks, Mistral AI, and Maven Clinic showcase how AI and robotics are moving from novelty to necessity. From Peggy Johnson’s Digit transforming warehouse labor, to Lenovo’s hybrid AI ecosystem, Databricks’ frictionless AI UIs, Mistral’s sovereignty-focused open-source models, and Maven’s virtual women’s health platform, this article explores the intelligent, personalized, and responsible future of tech. The next frontier of innovation isn’t just smart—it’s human-centered.
Global Shifts explores how leaders like Keyu Jin and Gregory Allen are analyzing the breakdown of old globalization models and the rise of new strategic paradigms. Jin outlines the emergence of regional economic blocs, China’s shift toward technology self-reliance, and the decentralization of capital. Allen frames AI as a strategic battleground, discussing export controls, the rise of DeepSeek, and the risks of decoupling. The piece offers a critical look at how economic power and innovation are evolving in an era defined by urgency, sovereignty, and competition.
In Technology, Climate Change and Justice, top leaders from Arm, The B Team, Vattenfall, and Silo AI outline how technology can both fuel and fix the climate crisis. From Leah Seligmann’s values-driven climate leadership to Anna Borg’s clean-energy grids and Peter Sarlin’s push for efficient, open-source AI, this piece highlights how innovation must align with inclusion, sustainability, and resilience. The message is clear: solving climate change isn’t just about new tech—it’s about how we deploy it, who benefits, and whether it truly serves a livable future.
In Innovation In Action, executives from Time, Sierra, and Axios share how they’re redefining business, media, and journalism with AI. Time is unlocking over a century of content for fair AI use, while Sierra’s “agentic AI” elevates the customer experience across industries. Axios emphasizes human-first reporting with AI support. Across the board, these leaders show how strategic adaptation can embrace AI without compromising trust, transparency, or editorial integrity.
The future of manufacturing is intelligent, autonomous, and sustainable. Powered by private 5G networks, AI, and digital twins, smart factories are revolutionizing how goods are produced and maintained. From predictive maintenance to immersive virtual twins and AI-optimized energy systems, smart manufacturing is unlocking new levels of efficiency and innovation across industries—from ports and shipyards to agriculture and healthcare.
Smart mobility is reshaping how the world moves, powered by 5G, AI, and edge computing. From autonomous vehicles and real-time logistics to AI-driven drones and connected public transport, intelligent transportation systems are redefining urban mobility, logistics, and industrial automation. As global investment and collaboration grow, the transportation industry is transforming into a $11.1 trillion smart ecosystem focused on sustainability, efficiency, and connectivity.

Download Magazine

With Subscription
Whitepaper
As VoLTE becomes the standard for voice communication, its rapid deployment exposes telecom networks to new security risks, especially in roaming scenarios. SecurityGen’s research uncovers key vulnerabilities like unauthorized access to IMS, SIP protocol threats, and lack of encryption. Learn how to strengthen VoLTE security with proactive measures such as...
Whitepaper
Dive into the comprehensive analysis of GTPu within 5G networks in our whitepaper, offering insights into its operational mechanics, strategic importance, and adaptation to the evolving landscape of cellular technologies....

It seems we can't find what you're looking for.

Subscribe To Our Newsletter

Scroll to Top