reinforcement learning

“The assumption underlying reinforcement learning Is that then there is some environment, and environment, you are an agent, and you are acting in environment by executing actions and getting rewards from the environment. And the rewards might be taught as, let…”

“Well, for instance, in terms of real-time execution, it takes something around three, three, three years of play to learn to play simple games . I mean it, it can be hugely parallelized, therefore it takes a few days to train it on current computers”

“So another way to do this is to use reinforcement learning. Yeah. And it, it's slower to train, because all you have is a single number, scalar reward, instead of this whole gradient flowing. Than GANs, but it also is more flexible.”

“The bot had, like, taught him the strategy that he could use against a human and I think that was, like, very interesting and a good example of the kinds of things that you can get out of these systems, that they can discover these very, sort of, you know, Non…”

“I believe there were three goals for the For the effort at the time. It was like, figure out how to do unsupervised learning, solve RL, and never get more than a 120 people. Missed on the third one.”

“What is the pre-training to RL the end-to-end training loop that's the next, you know, big sample? That I think is also what I think will happen in the next year. So I would say, if that is another scaling law breakthrough, because we will be, like, if you sor…”

“I think that the analog of synthetic data in language models is actually not necessarily simulation in robotics, but closer to something like reinforcement learning.”

“I think that reinforcement learning can play a very large role in it actually, in post-training. I think that online data from the robots which reinforcement learning allows you to use, Can allow robots to have a much higher success rate and also be faster tha…”

“Basically you can Scale up the compute in both pre-training and RL and get better and better performance. And I think that's sort of the fundamental thing that is driving AI progress. It's not that AI researchers are really smart or they suddenly got smart. It…”

“You can see scaling laws in the reinforcement learning phase of AI training.”

“You can get pretty big wins from RL. You sort of have another set of scaling laws. It's like you put more and more compute into RL, you can get better and better models out of that.”

“The, it's amazing how much of simulators, when you call environment.step, is still run on the CPU, and so that's usually the bottleneck for a lot of your on-policy rollouts”