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：强化学习（RL）在硅芯片物理设计中的应用（谷歌2021年《自然》论文）因声称证据不足而引发争议，其未经充分记录的声明不仅令人费解，还招致了批判性媒体报道。该论文隐瞒了关键方法论步骤及可复现结果的多数输入参数。我们的元分析表明，两项独立评估填补了这些空白，并证明谷歌强化学习在以下方面均表现落后：（i）人类设计师、（ii）经典算法（模拟退火）、（iii）通用商业软件，同时计算速度更慢；在2023年公开研究竞赛中，强化学习方法未能进入前五名。交叉验证数据显示，该《自然》论文的完整性因行为、分析和报告中的错误而受到严重损害。发表前，谷歌内部曾驳回关于学术不端的指控，这些指控至今仍悬而未决。我们指出相关芯片设计结论及其政策影响。