Reinforcement Learning as a fine-tuning paradigm

Re:Reinforcement Learning as a fine-tuning paradigm by armanboyaci on Wednesday January 12, 2022 @04:49AM (#29904298)

> Other learning paradigms are about minimization; reinforcement learning is about maximization.

I don't see why this is important.

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Re:Reinforcement Learning as a fine-tuning paradigm by Matumio on Wednesday January 12, 2022 @02:48PM (#29911884)

I think they wanted to express that learning to predict the correct output ("error minimization") puts a limit on the achievable performance. While ranking (not just RL, really) allows to improve beyond the current best-known answer.

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Re:Reinforcement Learning as a fine-tuning paradigm by patresh on Wednesday January 12, 2022 @05:16AM (#29904483)

Also the next point

> It should have (and has shown to have) better scaling laws

is a statement based on two anecdotes but I don't see a compelling reason why this should be the case in general.

Active learning approaches are not mentioned even though they allow incorporating human feedback during the fine-tuning process and this can be done with a purely supervised approach.

IMO the last point is the only compelling one : having for example agents that can browse the web during learning could open a lot of possibilities. It would have been interesting to develop this last point more : what are the current difficulties in training such agents?

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Re:Reinforcement Learning as a fine-tuning paradigm by ankeshanand on Wednesday January 12, 2022 @02:36PM (#29911742)

It's important in the context that RL does not have performance ceilings.

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