  Reinforcement learning (RL) for physical design of silicon chips in a Google
2021 Nature paper stirred controversy due to poorly documented claims that
raised eyebrows and drew critical media coverage. The paper withheld critical
methodology steps and most inputs needed to reproduce results. Our
meta-analysis shows how two separate evaluations filled in the gaps and
demonstrated that Google RL lags behind (i) human designers, (ii) a well-known
algorithm (Simulated Annealing), and (iii) generally-available commercial
software, while being slower; and in a 2023 open research contest, RL methods
weren't in top 5. Crosschecked data indicate that the integrity of the Nature
paper is substantially undermined owing to errors in conduct, analysis and
reporting. Before publishing, Google rebuffed internal allegations of fraud,
which still stand. We note policy implications and conclusions for chip design.
