Public evidence record

Kyle Corbitt

Published podcast speaker

Claims: 16
Episodes: 1
Shows: 1
Named items: 0

Claim ledger

What Kyle said.

3 transcript-backed records

01 / prediction

1 May 2026

I expect we'll see much more locking down models to specific use cases, specific product surfaces for multiple reasons, but a big one being the, because it makes distillation harder, especially distillation in, out of domain areas that aren't within that product surface.

“I expect we'll see much more locking down models to specific use cases, specific product surfaces for multiple reasons, but a big one being the, because it makes distillation harder, especially distillation in, out of domain areas that aren't within that product surface.”

Speaker: Kyle Corbitt
Episode: The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking
Publisher: The Cognitive Revolution

02 / prediction

1 May 2026

I think the rollout to the physical world would be very slow, probably just because there's a lot of constraints there and the data efficiency is going to be low, so the ROI is going to be relatively low and likely Frontier Labs is going to be very concentrated on automating everything digital first and then eventually potentially, there's these long tail, these physical things which are annoying to work with.

“I think the rollout to the physical world would be very slow, probably just because there's a lot of constraints there and the data efficiency is going to be low, so the ROI is going to be relatively low and likely Frontier Labs is going to be very concentrated on automating everything digital first and then eventually potentially, there's these long tail, these physical things which are annoying to work with.”

Speaker: Kyle Corbitt
Episode: The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking
Publisher: The Cognitive Revolution

03 / prediction

1 May 2026

My guess is probably not. So I think concretely for your task, if the trade-off you're making is, Hey, we're going to take an open-source model and use RL to try and make it better at this, versus, Hey, we're just going to take whatever the best off-the-shelf model is and do prompt engineering, and we're allowing ourselves to expand to the best frontier models.

“My guess is probably not. So I think concretely for your task, if the trade-off you're making is, Hey, we're going to take an open-source model and use RL to try and make it better at this, versus, Hey, we're just going to take whatever the best off-the-shelf model is and do prompt engineering, and we're allowing ourselves to expand to the best frontier models.”

Speaker: Kyle Corbitt
Episode: The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking
Publisher: The Cognitive Revolution