Reinforcement learning is becoming both the bottleneck and the unlock for the next generation of AI systems.
Over the past few years, we’ve seen remarkable progress in model capabilities driven by scale: more data, more compute, and better architectures. But as models begin to interact with the world, by using tools, navigating interfaces, and writing and executing code, the limiting factor is no longer just pretraining data. It is the environments, and their quality, in which these models can learn, practice, and be evaluated at scale.
Reinforcement learning (RL) environments are already becoming the next critical layer of the AI stack: a shift from static, human-annotated datasets to dynamic, engineered systems that generate high-quality training signals at scale. This transforms data from a labor problem into an engineering and research problem, and increasingly, a compute problem.
That’s why we’re excited to announce that we are leading the Series A in Deeptune, a company building leading RL environments for computer-use and code.
Deeptune is tackling a problem that sits at the heart of frontier AI development. As models move beyond text prediction into real-world task execution, they need structured environments where they can learn to fully control computers and perform knowledge work tasks. These environments must be realistic, measurable, and adaptable to rapidly evolving model capabilities.
This is extraordinarily difficult to get right.
Designing high-quality RL environments requires a deep understanding of both cutting-edge AI research and the practical needs of top labs. Tasks need to be representative of real-world complexity. Evaluation needs to be precise and reproducible. And the entire system needs to evolve quickly as models improve.
Deeptune stands out because they’ve built exactly this.
The company has been working closely with leading AI labs, developing environments for computer use that are already showing up in benchmark improvements like OSWorld (a computer use benchmark), Terminal-Bench (command-line interface benchmark) and beyond. As models trained in these environments become more capable, we’re starting to see meaningful gains in how effectively they can fully operate computers (via a command-line or a Desktop interface), an early signal of what’s to come.
While there is still significant room for improvement in computer use benchmarks, SOTA models have made rapid progress on computer use over the past year. Opus 4.6 scores 72.7% on OSWorld, surpassing the human baseline of 72.36%, and GPT-5.4 reaches 75%.
We got a glimpse of this future with systems like OpenClaw: models that can meaningfully interact with software, tool use, computer use, and complete multi-step tasks. These are early signals, but they point to a world where models are capable of completing tasks end-to-end entirely on a computer. RL environments are what make that transition possible.
At the center of Deeptune is Tim Lupo, the founder and CEO. Tim is an exceptional founder with a rare combination of technical depth and product intuition. He works closely with top researchers across leading AI labs and has a deep understanding of what they actually need. This shows up in Deeptune’s ability to ship high-quality environments, tasks, and evaluation frameworks at an unprecedented pace.
Building in this space requires tight feedback loops with frontier research, and Tim has built those relationships from day one.
He has also assembled an outstanding team. Deeptune brings together researchers and engineers from leading AI companies and top labs, combining deep expertise in machine learning, systems, and applied research. It is a group uniquely suited to build the infrastructure layer that modern RL demands.
What makes Deeptune particularly compelling is that they are not just building tools, they are helping define a new paradigm for how AI models are trained.If the last decade of AI progress was driven by better datasets, the next decade will be mostly driven by better environments.
We believe Deeptune is at the forefront of this shift.
We’re thrilled to partner with Tim and the Deeptune team as they build this critical layer of the AI stack.
