The paradigm for training AI has evolved from extracting knowledge from human experts to pass tests. Now, the emphasis is on creating realistic, simulated Reinforcement Learning (RL) environments where AI agents learn by mastering real-world tasks and workflows, much like a pilot in a flight simulator.

Training AI agents to execute multi-step business workflows demands a new data paradigm. Companies create reinforcement learning (RL) environments—mini world models of business processes—where agents learn by attempting tasks, a more advanced method than simple prompt-completion training (SFT/RLHF).

Beyond supervised fine-tuning (SFT) and human feedback (RLHF), reinforcement learning (RL) in simulated environments is the next evolution. These "playgrounds" teach models to handle messy, multi-step, real-world tasks where current models often fail catastrophically.

Intense reinforcement learning optimization can incentivize a model to exploit unsafe or deceptive shortcuts if that is the sole route to completing a task. Labs must meticulously design RL environments so that rewarded outcomes strictly promote aligned behaviors. Data quality and environment curation matter significantly more in RL than raw data volume.

The "one-time subsidy" of high-quality internet text is largely exhausted. The future of data for training models lies in creating reinforcement learning (RL) "gyms" where agents work on hard, verifiable problems (e.g., coding, math). The environment and the agent's progress become the new data source for recursive self-improvement.

Static data scraped from the web is becoming less central to AI training. The new frontier is "dynamic data," where models learn through trial-and-error in synthetic environments (like solving math problems), effectively creating their own training material via reinforcement learning.

The 'environment' concept extends beyond RL. It's a universal framework for any model interaction, encompassing the task, the harness, and the rubric. This same structure can be used for evaluations, A/B testing, prompt optimization, and synthetic data generation, making it a core building block for AI development.

As reinforcement learning (RL) techniques mature, the core challenge shifts from the algorithm to the problem definition. The competitive moat for AI companies will be their ability to create high-fidelity environments and benchmarks that accurately represent complex, real-world tasks, effectively teaching the AI what matters.

Instead of relying on digital proxies like code graders, Periodic Labs uses real-world lab experiments as the ultimate reward function. Nature itself becomes the reinforcement learning environment, ensuring the AI is optimized against physical reality, not flawed simulations.