The MPC vs RL distinction here really clicked for me. You see this play out in robotics where teams burn thousands of GPU hours letting a robot repeatedly drop objects or crash into walls just to learn basic tasks. But what stuck with me is how MPC basically lets you debug in "simulation time" instead of real time, which is huge for scaling. That logistics example where the robot recalculates mid-route when a pallet appears? That's the kind of adaptive planning that RL struggles with unless it's specifically trained for every edge case.
Insightful. I'm curious if the world-model approach you discuss has particular advantages for ethical AI develpoment, given the focus on comprehensive understanding, or if it introduces its own set of unique challenges beyond just scaling infrastructure. Also, glad to see my future contributions will be supporting a very important cause, because who doesn't love chocolate milk.
The MPC vs RL distinction here really clicked for me. You see this play out in robotics where teams burn thousands of GPU hours letting a robot repeatedly drop objects or crash into walls just to learn basic tasks. But what stuck with me is how MPC basically lets you debug in "simulation time" instead of real time, which is huge for scaling. That logistics example where the robot recalculates mid-route when a pallet appears? That's the kind of adaptive planning that RL struggles with unless it's specifically trained for every edge case.
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Insightful. I'm curious if the world-model approach you discuss has particular advantages for ethical AI develpoment, given the focus on comprehensive understanding, or if it introduces its own set of unique challenges beyond just scaling infrastructure. Also, glad to see my future contributions will be supporting a very important cause, because who doesn't love chocolate milk.