Thinking Machines Lab, backed by $2 billion in seed funding and staffed with former OpenAI researchers, has shared its first detailed research insights.
The lab released a blog post Wednesday examining how to create AI models that produce more consistent and reproducible responses, addressing a fundamental challenge in artificial intelligence development.
AI model consistency research targets nondeterminism in large language models
The blog post, titled “Defeating Nondeterminism in LLM Inference,” investigates why AI models often generate varied answers to identical questions. While this variability has been accepted as an inherent characteristic of large language models, Thinking Machines Lab views this nondeterminism as a solvable problem rather than an unavoidable limitation.
GPU kernel orchestration causes response randomness
Researcher Horace He authored the post, arguing that randomness in AI models stems from how GPU kernels are orchestrated during inference processing. Inference processing refers to the computational steps that occur after users submit queries, such as pressing enter in ChatGPT.
GPU kernels are specialized programs running on Nvidia computer chips. He believes careful management of this orchestration layer can enable AI models to generate more predictable and consistent outputs.
Consistent responses improve reinforcement learning training
Beyond enhancing reliability for enterprise and scientific applications, He suggests reproducible responses can streamline reinforcement learning (RL) training. Reinforcement learning rewards AI models for correct answers, but inconsistent responses introduce noise into training data.
More consistent responses could improve the RL process, which aligns with The Information’s previous reporting that Thinking Machines Lab plans to use RL for tailoring AI models to specific business needs.
First product launch planned for coming months
Former OpenAI Chief Technology Officer Mira Murati announced in July that Thinking Machines Lab will release its first product soon. She indicated the product will be “useful for researchers and startups developing custom models,” though specific details and whether it incorporates the reproducibility techniques remain undisclosed.
Open research commitment mirrors early OpenAI approach
Thinking Machines Lab announced plans to regularly publish blog posts, code, and research outputs to “benefit the public, but also improve our own research culture.” The recent post launches a new series called “Connectionism,” reflecting this transparency commitment.
This approach mirrors OpenAI’s early open research pledge, though OpenAI became less transparent as it grew. The research blog provides rare insight into Thinking Machines Lab’s operations and indicates the company is tackling significant AI research challenges while working toward products that justify its $12 billion valuation.
