Jin: RL enables models to surpass expert labelers and develop self-direction
Roger Jin · What is an RL environment? w/ Nous Research's Roger Jin · Apr 29, 2025 · at 6:50
This episode carries Roger Jin's own address, with nobody on the show putting questions to them. It still counts as said, and it is kept out of every score on their page.
Roger Jin of Nous Research highlights the advantages of reinforcement learning over supervised training for developing reasoning models.
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“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, thinking models that have learned their own approach to solving problems.”
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“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.”
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“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.”
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“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…”