Reinforcement Learning Environments
topic on 4 shows · 5 statements across 5 episodes
Latent Space 
the Neon Show 
Invest Like the Best 
20VC
5 statements about Reinforcement Learning Environments, every show
Krishnan: Human-designed prompt and auto-verifier tuples maximize synthetic training data ROI
“The, this method is the one, I think, which has a lot of legs in the, particularly the more you can operate in this particular paradigm of prompt and then these rule or rubric based verifier tuples. The, that is a very nice way for sort of creating synthetic d…”
Patel: CPUs are completely sold out driven by reinforcement learning demand
“CPU-wise, all these reinforcement learning environments plus all the slop code you and I are generating that is now running on some, you know, Vercel instance or whatever it is or some AWS instance or some bucket that we've spun up, all of that requires CPU, a…”
Patel: About 40 Bay Area startups are building RL environments
“And so there's like 40 startups now in the Bay doing these environments and, you know, questionable whether or not they'll, any of them will make it or what will happen.”
Reinforcement learning environments will subsume the entire economy
“RL environments will subsume the entire economy, because it doesn't make sense that humans would be doing monotonous, redundant work.”
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.”