As someone unfamiliar with the topic but trying to piece together information, I have to admit that they do a better job at convincing me of the potential of reinforcement in chip design.

As with most, if not all, applications of reinforcement learning, there are always traditional algorithms that outperform it. But that does not mean that the approach lacks promise, or is at least interesting.

Sure, the paper might have polished up some results, but if that is the case, it is better addressed through the appropriate channels. Engaging in public criticism does not build too much trust, at least not with this curious observer.

Hm... I also had to piece things together and agree that Google PR is pretty slick. The approach had promise 3-4 years ago, but the science seems clear now. Google is avoiding tests on shared chip designs but claims a breakthrough. There is no breakthrough as everyone is still using Cadence or Synopsys software tools.

Maybe you can make RL work for chip design at some point, but if the paper "polished up some results", why is it still getting any respect? You are right about "appropriate channels", that's what Chatterjee and Kahng tried, but Chatterjee was fired by Google as a whistleblower (red flag!) while Kahng is getting flak even these comments (another red flag!). Where would you look next as an independent observer?