Reinforcement Learning: AI’s Next Big Leap

Lately, I keep coming back to reinforcement learning. It’s been the focus of the last few Hidden Layer episodes for a reason. I’m convinced it’s not just another tool in the AI toolbox. It’s a turning point.

Supervised learning has taken us far, but it’s starting to hit a ceiling. Models can only go so far by mimicking human examples. Reinforcement learning opens the door to something different. It’s how we move from copying intelligence to creating it.

Think of it like this. Supervised learning is flashcards. Reinforcement learning is the real world. A model tries, fails, gets feedback, adjusts, and improves. It’s how AlphaGo Zero discovered strategies no human had ever considered. It’s how Absolute Zero learned to code by creating its own problems and then solving them.

We’re already seeing signs of this shift. The big labs are investing heavily in reinforcement learning. I’d bet that by 2027, it will take up the lion’s share of AI training budgets. Pretraining will be just the start, not the focus.

Reinforcement learning isn’t just a new technique. It’s the beginning of a new phase in AI.

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