Reasoning

From Vibe Coding to Vibe Researching: OpenAI’s Mark Chen and Jakub Pachocki

From Vibe Coding to Vibe Researching: OpenAI’s Mark Chen and Jakub Pachocki

OpenAI’s Chief Scientist, Jakub Pachocki, and Chief Research Officer, Mark Chen, discuss the research behind GPT-5, the push toward long-horizon reasoning, and the grand vision of an automated researcher. They cover how OpenAI evaluates progress beyond saturated benchmarks, the surprising durability of reinforcement learning, and the culture required to protect fundamental research while shipping world-class products.

AGI progress, surprising breakthroughs, and the road ahead — the OpenAI Podcast Ep. 5

AGI progress, surprising breakthroughs, and the road ahead — the OpenAI Podcast Ep. 5

OpenAI's Chief Scientist Jakub Pachocki and researcher Szymon Sidor discuss the rapid progress towards AGI, focusing on the shift from traditional benchmarks to real-world capabilities like automating scientific discovery. They share insights into recent breakthroughs in mathematical and programmatic reasoning, highlighted by successes in competitions like the International Math Olympiad (IMO), and explore what's next for scaling and long-horizon problem-solving.

Introducing GPT-5

Introducing GPT-5

OpenAI introduces GPT-5, a significant upgrade focused on expert-level reasoning, agentic capabilities, and real-world utility, particularly for developers and enterprises. The model introduces a new reasoning paradigm, "software on demand" capabilities, and state-of-the-art performance on coding, reasoning, and long-context benchmarks. The launch also includes major updates to the ChatGPT application and a powerful new API for developers.

OpenAI’s IMO Team on Why Models Are Finally Solving Elite-Level Math

OpenAI’s IMO Team on Why Models Are Finally Solving Elite-Level Math

Members of the OpenAI team, Alex Wei, Sheryl Hsu, and Noam Brown, discuss their model's historic gold-medal performance at the International Mathematical Olympiad (IMO). They detail their unique approach using general-purpose reinforcement learning for hard-to-verify tasks, the model's surprising self-awareness, and the vast gap that remains between solving competition problems and achieving true mathematical research breakthroughs.