Algorithms like GRPO are powerful but require parallel rollouts in a reproducible environment. Building and maintaining these high-fidelity sandboxes, complete with realistic data and failure modes, is the hardest part of implementing RL today and a significant barrier for most companies.

There's a significant gap between AI performance in simulated benchmarks and in the real world. Despite scoring highly on evaluations, AIs in real deployments make "silly mistakes that no human would ever dream of doing," suggesting that current benchmarks don't capture the messiness and unpredictability of reality.

Pre-training on internet text data is hitting a wall. The next major advancements will come from reinforcement learning (RL), where models learn by interacting with simulated environments (like games or fake e-commerce sites). This post-training phase is in its infancy but will soon consume the majority of compute.

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.

The choice between simulation and real-world data depends on a task's core difficulty. For locomotion, complex reactive behavior is harder to capture than simple ground physics, favoring simulation. For manipulation, complex object physics are harder to simulate than simple grasping behaviors, favoring real-world data.

A niche, services-heavy market has emerged where startups build bespoke, high-fidelity simulation environments for large AI labs. These deals command at least seven-figure price tags and are critical for training next-generation agentic models, despite the customer base being only a few major labs.

Instead of simulating photorealistic worlds, robotics firm Flexion trains its models on simplified, abstract representations. For example, it uses perception models like Segment Anything to 'paint' a door red and its handle green. By training on this simplified abstraction, the robot learns the core task (opening doors) in a way that generalizes across all real-world doors, bypassing the need for perfect simulation.

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.