Our work focuses on the practical engineering of large foundation models optimized for high-level reasoning capabilities.
On top of these models, we prototype AI agents that can accomplish goals on our behalf, starting with agents that help us code. We believe seriously using these agents to accelerate our own work is necessary, in order to shed light on how to improve both the underlying capabilities of our reasoning models, and on interaction design for agents.
Ultimately, we hope to release systems that enable anyone to build robust, custom AI agents that can accomplish larger goals and safely work for us in the real world.
We’re excited to announce our latest funding round, a $200M Series B at a valuation of over $1 billion, with participation from Astera…Read more
In this post, we introduce CARBS, a cost-aware hyperparameter optimizer that: Automatically reproduces the Chinchilla scaling law for…Read more
To build reasoning models that provide a robust foundation for AI agents, we work on:
Critically, our full-stack approach unlocks feedback loops designed to speed up our work. Designing agents and tools helps us build better models, in turn unlocking even more useful agents that enable even better models.