NVIDIA and Ineffable Intelligence Join Forces to Revolutionize Reinforcement Learning Infrastructure

Reinforcement learning (RL) is changing how AI systems learn, moving beyond static datasets to dynamic, experience-driven discovery. To support this paradigm shift, NVIDIA has partnered with Ineffable Intelligence—a London-based AI lab founded by AlphaGo pioneer David Silver—to co-develop the infrastructure needed for large-scale RL. This collaboration aims to build a high-performance pipeline that can handle the unique demands of RL workloads, where data is generated on the fly through continuous trial-and-error loops. Below, we explore key aspects of this partnership through a series of questions and answers.

What is the core focus of the NVIDIA–Ineffable Intelligence collaboration?

The collaboration targets the design of a next-generation infrastructure specifically for large-scale reinforcement learning. Unlike traditional AI training that relies on fixed human-curated datasets, RL agents generate their own training data by interacting with environments, observing outcomes, and adjusting behavior. This requires a tightly integrated pipeline where acting, observing, scoring, and updating happen in rapid succession. NVIDIA and Ineffable are pooling engineering resources to optimize interconnect, memory bandwidth, and serving systems to handle these real-time demands. The ultimate goal is to create a scalable platform that can support RL experiments in complex, rich environments—unlocking breakthroughs across science, engineering, and beyond.

Who are the key players, and what expertise do they bring?

NVIDIA is a global leader in accelerated computing and AI hardware, known for its GPUs and platforms like Grace Blackwell and Vera Rubin. Ineffable Intelligence, founded by David Silver—co-creator of AlphaGo and a pioneer in reinforcement learning—brings deep algorithmic expertise. Silver’s vision is to move beyond systems that replicate human knowledge toward “superlearners” that continuously discover new knowledge from experience. Jensen Huang, NVIDIA’s CEO, expressed excitement about co-designing RL infrastructure with Ineffable, emphasizing that this partnership pushes the frontier of AI by enabling systems that learn continuously. The combination of cutting-edge hardware and world-class RL research positions the collaboration to tackle challenges no single entity could solve alone.

What makes reinforcement learning infrastructure different from pretraining infrastructure?

In pretraining, a fixed dataset of human-generated data flows through the system in a linear fashion. Reinforcement learning, by contrast, is a dynamic loop: the agent acts, receives observations and rewards, evaluates performance, and updates its model—all in real time. This puts immense pressure on three key areas: interconnect (moving data between compute nodes quickly), memory bandwidth (storing and accessing intermediate results), and serving (handling inference requests for each action). Moreover, RL systems often train on novel forms of experience (e.g., simulation data) that differ from human language, requiring new model architectures and training algorithms. The infrastructure must be optimized for low latency and high throughput to keep the learning loop efficient.

How will NVIDIA and Ineffable approach building this new pipeline?

Engineers from both companies are working together to explore the best architecture for a large-scale RL training pipeline. They start with NVIDIA’s Grace Blackwell platform—a superchip combining Grace CPUs and Blackwell GPUs—and will soon extend to the upcoming Vera Rubin platform. The collaboration focuses on understanding the specific hardware and software requirements for RL: how to minimize latency in the action-observation-update cycle, how to efficiently distribute simulation and training across many nodes, and how to handle the unique memory access patterns of RL workloads. By iterating on these designs, they aim to create a reference infrastructure that can accelerate RL research and deployment, eventually making it as accessible as traditional deep learning is today.

What hardware platforms are being used, and why are they significant?

The work begins on NVIDIA Grace Blackwell, a platform designed for AI workloads that demand high memory bandwidth and low power consumption. Grace Blackwell combines Arm-based Grace CPUs with Hopper-architecture GPUs to enable efficient data movement—critical for RL’s tight feedback loops. The collaboration will then explore the upcoming NVIDIA Vera Rubin platform, which promises even more advanced memory and interconnect technologies. Being among the first to test these platforms gives the team a head start in shaping the next generation of AI hardware. The goal is to determine what architectural features are most beneficial for RL, such as faster interconnects for multi-agent systems or specialized memory for simulation environments.

What is the ultimate goal of this reinforcement learning infrastructure?

The endgame is to unlock “unprecedented scale” of reinforcement learning in highly complex and rich environments—from robotics and game-playing to scientific discovery and medical research. By building an optimized infrastructure, agents will be able to learn from billions of interactions, exploring strategies that humans may never conceive. David Silver envisions a future where AI systems continuously discover new knowledge, not just mimic what we already know. This infrastructure could accelerate breakthroughs in materials science, drug design, climate modeling, and more. In short, the partnership aims to turn computation into a tool for generating new knowledge, fundamentally expanding what AI can achieve.

Why did David Silver found Ineffable Intelligence in the first place?

David Silver emerged from stealth just last week with a clear mission: to solve the “harder problem of AI”—building systems that discover new knowledge from experience, rather than simply learning from human data. As he noted, researchers have largely mastered teaching AI what humans already know; now the frontier is enabling machines to learn from their own actions. Ineffable Intelligence focuses on reinforcement learning as the path to this new paradigm. The collaboration with NVIDIA provides the computational backbone needed to realize Silver’s vision at scale, combining his algorithmic breakthroughs with NVIDIA’s hardware prowess.