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.

翻译：谷歌在2021年《自然》杂志上发表的一篇关于采用强化学习（RL）进行硅芯片物理设计的论文引发了争议，其论证不足的论断令人侧目并招致了媒体的批评性报道。该论文隐瞒了关键的方法步骤和复现结果所需的大部分输入数据。我们的荟萃分析表明，两项独立的评估填补了这些空白，并证明谷歌的强化学习方法在性能上落后于（i）人类设计师，（ii）一种知名算法（模拟退火法），以及（iii）普遍可用的商业软件，同时速度更慢；在2023年的一项公开研究竞赛中，强化学习方法未能进入前五名。交叉核对的数据表明，由于在实验操作、数据分析和结果报告方面存在错误，《自然》杂志该论文的完整性受到了严重损害。在发表前，谷歌驳回了内部关于欺诈的指控，这些指控至今仍未解决。我们指出了该事件对芯片设计领域的政策启示与结论。