[iTokio]

I skimmed through the paper and the code and got the same conclusion.

It’s overhyped, filled with marketing language.

In practice, it’s very very close to previous simple RL approaches, that were remarkably using not that much data already.

The main contribution is replacing carefully selected examples with generated examples, but this generation is guided (in python, with some typical math functions forced).

It’s akin to replacing some manual tests with mutation testing.

Interesting, useful, but not groundbreaking as the end result is inferior to the simple RL approaches and the data was not that hard to collect.

It is an interesting approach to generalize to other domains where there might be less data available or less easy to curate