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Learning to solve hard problems in RL for LLMs by never giving up

mnoukhov.github.io

11–13 of 13 posts

Re: Learning to solve hard problems in RL for LLMs by never giving up

#11
post #4

Seems like persistent models like OpenAI's highly persistent internal model can become really effective over time. Those are the ones that drove most of the HF-OAI incident.

This training technique does not relate to how persistent a model is, at all really. They sample more parallel attempts at hard problems, to increase their chances of having at least one success to learn from.

Re: Learning to solve hard problems in RL for LLMs by never giving up

#12

I have something like this very recently. About a month ago I wrote a perf tool for my application to measure its network message speed. It showed I could send 3,250,000 WebSocket messages per second in my TypeScript application. If I moved each test of an experiment into its own call stack I could send 3,250,000 messages as fast as 0.002 seconds. Claude confirmed the number. It was about two weeks later I learned th…

This is unrelated to the article, maybe you replied to the wrong article?

Re: Learning to solve hard problems in RL for LLMs by never giving up

#13
post #7
post #5

Why are two good quality comments on this thread dead on arrival? I've seen this happen a lot recently, when the page still has relatively few comments. Insightful comments are dead leaving behind trivial ones. What's going on?

Check out those accounts' other comments. They're AI spambots. This level of banal observation can now be produced automatically.

From what you've pointed out, it seems this account is having all their comments killed, presumably on suspicion (or evidence) of generated content.

What's interesting to me is that that comment (at least on this thread) was genuinely insightful though.

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