AI’s Path Ahead: Reinforcement Learning Environments

For the past decade, progress in artificial intelligence has been measured by scale: bigger models, larger datasets, and more compute. That approach delivered astonishing breakthroughs in large language models (LLMs); in just five years, AI has leapt from models like GPT-2, which could hardly mimic coherence, to systems like GPT-5 that can reason and engage in substantive dialogue. And now early prototypes of AI agents that can navigate codebases or browse the web point towards an entirely new frontier.

But size alone can only take AI so far. The next leap won’t come from bigger models alone. It will come from combining ever-better data with worlds we build for models to learn in. And the most important question becomes: What do classrooms for AI look like?

In the past few months Silicon Valley has placed its bets, with labs investing billions in constructing such classrooms, which are called reinforcement learning (RL) environments. These environments let machines experiment, fail, and improve in realistic digital spaces.

AI Training: From Data to Experience

The history of modern AI has unfolded in eras, each defined by the kind of data that the models consumed. First came the age of pretraining on internet-scale datasets. This commodity data allowed machines to mimic human language by recognizing statistical patterns. Then came data combined with reinforcement learning from human feedback—a technique that uses crowd workers to grade responses from LLMs—which made AI more useful, responsive, and aligned with human preferences.

We have experienced both eras firsthand. Working in the trenches of model data at Scale AI exposed us to what many consider the fundamental problem in AI: ensuring that the training data fueling these models is diverse, accurate, and effective in driving performance gains. Systems trained on clean, structured, expert-labeled data made leaps. Cracking the data problem allowed us to pioneer some of the most critical advancements in LLMs over the past few years.

Today, data is still a foundation. It is the raw material from which intelligence is built. But we are entering a new phase where data alone is no longer enough. To unlock the next frontier, we must pair high-quality data with environments that allow limitless interaction, continuous feedback, and learning through action. RL environments don’t replace data; they amplify what data can do by enabling models to apply knowledge, test hypotheses, and refine behaviors in realistic settings.

How an RL Environment Works

In an RL environment, the model learns through a simple loop: it observes the state of the world, takes an action, and receives a reward that indicates whether that action helped accomplish a goal. Over many iterations, the model gradually discovers strategies that lead to better outcomes. The crucial shift is that training becomes interactive—models aren’t just predicting the next token but improving through trial, error, and feedback.

For example,…