RL works great on verifiable domains like math, and to some significant extent coding.
Coding is an interesting example because as we change levels of abstraction from the syntax of a specific function to, say, the architecture of a software system, the ability to measure verifiable correctness declines. As a result, RL-tuned LLMs are better at creating syntactically correct functions but struggle as the abstraction layer increases.
In other fields, it is very difficult to verify correctness. What is good art? Here, LLMs and their ilk can still produce good output, but it becomes hard to produce "superhuman" output, because in nonverifiable domains their capability is dependent on mimicry; it is RL that gives the AI the ability to perform at superhuman levels. With RL, rather than merely fitting its parameters to a set of extant data it can follow the scent of a ground truth signal of excellence. No scent, no outperformance.
Coding is an interesting example because as we change levels of abstraction from the syntax of a specific function to, say, the architecture of a software system, the ability to measure verifiable correctness declines. As a result, RL-tuned LLMs are better at creating syntactically correct functions but struggle as the abstraction layer increases.
In other fields, it is very difficult to verify correctness. What is good art? Here, LLMs and their ilk can still produce good output, but it becomes hard to produce "superhuman" output, because in nonverifiable domains their capability is dependent on mimicry; it is RL that gives the AI the ability to perform at superhuman levels. With RL, rather than merely fitting its parameters to a set of extant data it can follow the scent of a ground truth signal of excellence. No scent, no outperformance.