So, a deployment of your agent uh in the real world generates data. That data then grounds the simulator and makes it more realistic. The simulator generates harder edge cases for the critic to score and for the agent to learn from.
Yeah, we we could do all of those things. Um it's just not better. And we simulate it all. they're in our simulator provably worse and so we wouldn't do it.
Swift’s build infra is good enough for most things these days. codex knows how to run iOS apps and how to deal with the Simulator. No special stuff or MCPs needed.
reinforcement learning is about understanding your world whereas large language models are about mimicking people doing what people say you should do. They're not about figuring out what to do.
but something to remember is that when a pilot is using a simulator to learn to fly an airplane, they're extremely goal- directed. So, their goal in life is not to learn to use a simulator. Their goal in life is to learn to fly the airplane.
I don’t think we’ve replicated human intelligence, unless I know that the simulator is making exactly the same kinds of mistakes that people do, because people make characteristic mistakes. They have characteristic biases, they have characteristic heuristics that we use, and those have yet to see evidence that ChatGPT will do that.
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