IA2's preprocessing creates a rich workload model for its deep reinforcement learning task. This model doesn't just analyze queries; it integrates query plans, current indexes, database metadata, and tokenized queries. This holistic state representation is key to its ability to generalize across diverse database workloads, providing a more accurate view of the system's state.

Rather than programming AI agents with a company's formal policies, a more powerful approach is to let them observe thousands of actual 'decision traces.' This allows the AI to discover the organization's emergent, de facto rules—how work *actually* gets done—creating a more accurate and effective world model for automation.

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.

In domains like coding and math where correctness is automatically verifiable, AI can move beyond imitating humans (RLHF). Using pure reinforcement learning, or "experiential learning," models learn via self-play and can discover novel, superhuman strategies similar to AlphaGo's Move 37.

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).

AI labs like Anthropic find that mid-tier models can be trained with reinforcement learning to outperform their largest, most expensive models in just a few months, accelerating the pace of capability improvements.

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.

Instead of relying on expensive, omni-purpose frontier models, companies can achieve better performance and lower costs. By creating a Reinforcement Learning (RL) environment specific to their application (e.g., a code editor), they can train smaller, specialized open-source models to excel at a fraction of the cost.

When determining what data an RL model should consider, resist including every available feature. Instead, observe how experienced human decision-makers reason about the problem. Their simplified mental models reveal the core signals that truly drive outcomes, leading to more stable, faster-learning, and more interpretable AI systems.