Roger Jin
On 1 show, 0 appearances, plus 1 compilation re-air not counted. The Shows tab opens the full record on each.
1shows
0appearances
9statements
0resolved
0supported
0contradicted
Everything Roger Jin said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Jin: RL enables models to surpass expert labelers and develop self-direction
“The model outperforming expert labelers is, is possible. The model learning, like, self-direction is, like, expected. And yeah, we've seen, like, kind of cool emergent behaviors with, like, you know, like, O-one, O-three, R-one, kind of, like, these, like, thi…”
Jin: Supervised Learning Cannot Train Models by Demonstrating Bad Examples
“There's, like, a kind of deficit, which is, like, there's no way to demonstrate what is bad in supervised learning. You kind of just, you have to tell the model very well, like, what is good.”
Jin: Supervised Models Rarely Outperform Expert Human Labelers
“There are, like, kind of surprising results, like weak to strong generalization, but, like, in general, like, the model outperforming the expert labelers would be, like, kind of very surprising.”
Jin: Policy gradient algorithms function as weighted supervised fine-tuning
“If you kind of, like, look at, if you kind of stare at, like, this part it sort of looks like just, like, weighted supervised fine-tuning, right? Like, you have this, like, log of, like, the probability of a token and, like, some, like, weight on it and reinfo…”
Jin: Open source needs a standard to scale RL environments
“And so, like, there just needs to be some kind of, like, standard for open source developers to all, like, work together to, like, build up this, you know, to scale environments up to, like, millions and millions of environments.”
Jin: Fusing inference and scoring natively enables multi-step and multi-agent RL
“Collect trajectories is a fusion of both these. It handles both inference and scoring, and we deliberately chose that because, like, what happens when you try to, like, do, like, multi-turn, or, like, multi-agent with, like, a separate score function? Then thi…”
Jin: Language models map directly to reinforcement learning policies
“So the states are, like, the text prefixes, so the initial states, like, the prompt the actions are the next tokens that means, like, a language model is, like, exactly what a policy is, right? A policy maps a state to a probability distribution of our next ac…”
Jin: Nous RL environments return literal tokens instead of parsed text
“Another, like, kind of weird quirky thing about our design is that at least for text, the thing that's returned by each of these environments is, like, the literal tokens. So it's not, like, it's not text, it's not, like, messages, it's the tokens.”
Jin: Token-level RL enables mixing instruct and base model fine-tuning
“Another thing you can do, like, with, in, in, like, the token world is, like, the trainer is now, like, agnostic to, like, chat versus instruct model. And what that means is, like, you can do all, like, the cool, like, R-one-zero kind of style, like, experimen…”
One line per show, most statements first. The link opens Roger's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 LATENT SPACELEDGER | 0 +1 | 9 | — | full record on Latent Space → |