Kevin Wang
speaker
180 appearances
1 recordings
1 series
first heard Jan 2026
last heard 2 Jan
Kevin Wang’s voice in public audio — every appearance, attributed to the second.
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Um so this is uh personally I've I'd be very curious about how a work like this could impact like the robotics field.
Like my understanding of robotics is that a lot of robotics are now there's kind of multi a few different approaches.
Like one approach is we want to train robots using imitation learning.
So we try to collect like an insane amount of data.
We have a ton of Cuban SIP supervision.
And we try to scale up this data and we're like learning with imitation learning.
Like but on the other hand.
Potential, like perhaps there's another approach, which is like, for example, like goal conditioned reinforcement learning, where we can actually train robotic agents and train RL agents to solve meaningful tasks with absolutely no human supervision, no demonstration.
So yeah, so this could serve as an alternate approach.
And perhaps instead of like scaling data, like scaling manual, like human supervision, which which is you know not super scalable, if there are ways to sort of
make full condition reinforcement learning scalable and like we can just scale the architecture or we can scale
Right.
With with certain different objectives.
I think that could be very exciting and see how to see how that can affect a field like robotics, for example.
others.
Yeah, I guess like
to your original question of like the trade-offs.
Um like one of the trade-offs, one of the limitations that we say is like obviously if you make the networks bigger, the it it will takes longer to run, right?
So if you like double the depth at some level of depth, you you it might take like twice as much to like take make a forward pass th through the network.
Right.
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